3210 lines
153 KiB
HTML
3210 lines
153 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<meta name="description" content="ML systems degrade gradually rather than fail suddenly, so we need error budgets for model accuracy, data freshness, and fairness — not just uptime.">
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<meta name="keywords" content="Site reliability engineering, framework">
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<meta property="og:description" content="ML systems degrade gradually rather than fail suddenly, so we need error budgets for model accuracy, data freshness, and fairness — not just uptime.">
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<meta property="og:site_name" content="dzone.com">
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<meta property="og:title" content="Building SRE Error Budgets for AI/ML Workloads">
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<meta property="og:url" content="https://dzone.com/articles/building-sre-error-budgets-for-ai-ml-workloads">
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<meta property="og:image" content="https://dz2cdn1.dzone.com/storage/article-thumb/18846864-thumb.jpg">
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<meta property="og:type" content="article">
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<meta name="twitter:site" content="@DZoneInc">
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<meta name="twitter:image" content="https://dz2cdn1.dzone.com/storage/article-thumb/18846864-thumb.jpg">
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<meta name="twitter:card" content="summary_large_image">
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<meta name="twitter:description" content="ML systems degrade gradually rather than fail suddenly, so we need error budgets for model accuracy, data freshness, and fairness — not just uptime.">
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<meta name="twitter:title" content="Building SRE Error Budgets for AI/ML Workloads">
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<meta name="referrer" content="origin-when-cross-origin">
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<meta name="google-site-verification" content="kndbhxcupfEqWmZclhCpB6vlgOs7QSmx2UHAGGnP2mA">
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<meta name="df-verify" content="df0d76632b4543">
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<link rel="icon" type="image/x-icon" href="https://dz2cdn1.dzone.com/themes/dz20/images/favicon.png">
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<link rel="image_src" href="https://dz2cdn1.dzone.com/storage/article-thumb/18846864-thumb.jpg">
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<link rel="canonical" href="https://dzone.com/articles/building-sre-error-budgets-for-ai-ml-workloads">
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<title>Building SRE Error Budgets for AI/ML Workloads</title>
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<link rel="preload" href="https://dz2cdn1.dzone.com/themes/dz20/font/fontello.woff?11773374" as="font" type="font/woff" crossorigin="anonymous">
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<link rel="stylesheet" media="all" href="https://dz2cdn1.dzone.com/themes/dz20/ftl/icons.css">
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<link rel="stylesheet" media="all" href="https://dz2cdn1.dzone.com/themes/dz20/lib/static/bootstrap/bootstrap.min.css">
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<link rel="stylesheet" media="all" href="https://dz2cdn1.dzone.com/themes/dz20/ftl/article/global.css">
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<link rel="stylesheet" media="all" href="https://dz2cdn1.dzone.com/themes/dz20/ftl/header-updated/styles.css">
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<style>
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:root {
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--xs-size: 2px;
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--sm-size: 5px;
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--md-size: 10px;
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--lg-size: 15px;
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--xl-size: 25px;
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--sm-border-size: 8px;
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--sm-font-size: 13px;
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--sm-plus-font-size: 16px;
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--md-font-size: 18px;
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--lg-font-size: 22px;
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--xl-font-size: 26px;
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}
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/* Display Quick-Access Classes */
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.display-none { display: none; }
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.display-block { display: block; }
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.display-inline { display: inline; }
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.display-inline-block { display: inline-block; }
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/* Margin Quick-Access Classes */
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.m-xs { margin: var(--xs-size); }
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.m-sm { margin: var(--sm-size); }
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.m-md { margin: var(--md-size); }
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.m-lg { margin: var(--lg-size); }
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.m-xl { margin: var(--xl-size); }
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.mt-xs, .my-xs { margin-top: var(--xs-size); }
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.mr-xs, .mx-xs { margin-right: var(--xs-size); }
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.mb-xs, .my-xs { margin-bottom: var(--xs-size); }
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.ml-xs, .mx-xs { margin-left: var(--xs-size); }
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.mt-sm, .my-sm { margin-top: var(--sm-size); }
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.mr-sm, .mx-sm { margin-right: var(--sm-size); }
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.mb-sm, .my-sm { margin-bottom: var(--sm-size); }
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.ml-sm, .mx-sm { margin-left: var(--sm-size); }
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.mt-md, .my-md { margin-top: var(--md-size); }
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.mr-md, .mx-md { margin-right: var(--md-size); }
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.mb-md, .my-md { margin-bottom: var(--md-size); }
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.ml-md, .mx-md { margin-left: var(--md-size); }
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.mt-lg, .my-lg { margin-top: var(--lg-size); }
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.mr-lg, .mx-lg { margin-right: var(--lg-size); }
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.mb-lg, .my-lg { margin-bottom: var(--lg-size); }
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.ml-lg, .mx-lg { margin-left: var(--lg-size); }
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.mt-xl, .my-xl { margin-top: var(--xl-size); }
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.mr-xl, .mx-xl { margin-right: var(--xl-size); }
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.mb-xl, .my-xl { margin-bottom: var(--xl-size); }
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.ml-xl, .mx-xl { margin-left: var(--xl-size); }
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.ml-auto, .mx-auto { margin-left: auto; }
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.mr-auto, .mx-auto { margin-right: auto; }
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.mt-auto, .my-auto { margin-top: auto; }
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.mb-auto, .my-auto { margin-bottom: auto; }
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.my-none, .mt-none { margin-top: 0 !important; }
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.mx-none, .mr-none { margin-right: 0 !important; }
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.my-none, .mb-none { margin-bottom: 0 !important; }
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.mx-none, .ml-none { margin-left: 0 !important; }
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/* Padding Quick-Access Classes */
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.p-xs { padding: var(--xs-size); }
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.p-sm { padding: var(--sm-size); }
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.p-md { padding: var(--md-size); }
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.p-lg { padding: var(--lg-size); }
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.p-xl { padding: var(--xl-size); }
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.pt-xs, .py-xs { padding-top: var(--xs-size); }
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.pr-xs, .px-xs { padding-right: var(--xs-size); }
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.pb-xs, .py-xs { padding-bottom: var(--xs-size); }
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.pl-xs, .px-xs { padding-left: var(--xs-size); }
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.pt-sm, .py-sm { padding-top: var(--sm-size); }
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.pr-sm, .px-sm { padding-right: var(--sm-size); }
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.pb-sm, .py-sm { padding-bottom: var(--sm-size); }
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.pl-sm, .px-sm { padding-left: var(--sm-size); }
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.pt-md, .py-md { padding-top: var(--md-size); }
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.pr-md, .px-md { padding-right: var(--md-size); }
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.pb-md, .py-md { padding-bottom: var(--md-size); }
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.pl-md, .px-md { padding-left: var(--md-size); }
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.pt-lg, .py-lg { padding-top: var(--lg-size); }
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.pr-lg, .px-lg { padding-right: var(--lg-size); }
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.pb-lg, .py-lg { padding-bottom: var(--lg-size); }
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.pl-lg, .px-lg { padding-left: var(--lg-size); }
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.pt-xl, .py-xl { padding-top: var(--xl-size); }
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.pr-xl, .px-xl { padding-right: var(--xl-size); }
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.pb-xl, .py-xl { padding-bottom: var(--xl-size); }
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.pl-xl, .px-xl { padding-left: var(--xl-size); }
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.py-none, .pt-none { padding-top: 0 !important; }
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.px-none, .pr-none { padding-right: 0 !important; }
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.py-none, .pb-none { padding-bottom: 0 !important; }
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.px-none, .pl-none { padding-left: 0 !important; }
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/* Flex Quick-Access Classes */
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.flex {
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display: flex;
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}
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.flex-column {
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display: flex;
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flex-direction: column;
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}
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.flex-column-reverse {
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display: flex;
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flex-direction: column-reverse;
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||
}
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.flex-row {
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display: flex;
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flex-direction: row;
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}
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.flex-row-reverse {
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display: flex;
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flex-direction: row-reverse;
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}
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.flex-wrap {
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flex-wrap: wrap;
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}
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.flex-grow {
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flex-grow: 1;
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}
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.align-center { align-items: center; }
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.align-end { align-items: flex-end; }
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.content-center {
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align-content: center;
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}
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.justify-center { justify-content: center; }
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.justify-start { justify-content: flex-start; }
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.justify-end { justify-content: flex-end; }
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.justify-around { justify-content: space-around; }
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.justify-between { justify-content: space-between; }
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.justify-evenly { justify-content: space-evenly; }
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.gap-sm { gap: var(--sm-size); }
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.gap-md { gap: var(--md-size); }
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.gap-lg { gap: var(--lg-size); }
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.gap-xl { gap: var(--xl-size); }
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/* Display Quick-Access Classes */
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||
.inline-block { display: inline-block; }
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||
|
||
/* Font Quick-Access Classes */
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||
.font-xl { font-size: var(--xl-font-size); }
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.font-lg { font-size: var(--lg-font-size); }
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.font-md { font-size: var(--md-font-size); }
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.font-sm-plus { font-size: var(--sm-plus-font-size); }
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.font-sm { font-size: var(--sm-font-size); }
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.font-strike { text-decoration: line-through; }
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||
.font-underline { text-decoration: underline; }
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.font-overline { text-decoration: overline; }
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.font-italic { font-style: italic; }
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||
.font-bold { font-weight: bold; }
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.font-black { color: #000000; }
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.font-white { color: #ffffff; }
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.font-gray { color: var(--brand-gray-text); }
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.font-danger { color: var(--header-dropdown-button-bg-danger); }
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||
.font-center { text-align: center; }
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.no-wrap { white-space: nowrap; }
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||
/* Consistent headings between h and non-h tags */
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.heading {
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margin-top: 0;
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margin-bottom: var(--md-size);
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}
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.heading.font-xl { line-height: var(--xl-font-size); }
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.heading.font-lg { line-height: var(--lg-font-size); }
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.heading.font-md { line-height: var(--md-font-size); }
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.heading.font-sm-plus { line-height: var(--sm-plus-font-size); }
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||
.heading.font-sm { line-height: var(--sm-font-size); }
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||
|
||
/* Font Clamp Quick-Access Classes */
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||
.line-clamp,
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||
.line-clamp-2,
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||
.line-clamp-3,
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||
.line-clamp-4,
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||
.line-clamp-5 {
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||
display: -webkit-box;
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||
-webkit-line-clamp: 4;
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||
-webkit-box-orient: vertical;
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||
overflow: hidden;
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||
text-overflow: ellipsis;
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||
}
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||
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.line-clamp-2 { -webkit-line-clamp: 2 !important; }
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||
.line-clamp-3 { -webkit-line-clamp: 3 !important; }
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||
.line-clamp-4 { -webkit-line-clamp: 4 !important; }
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||
.line-clamp-5 { -webkit-line-clamp: 5 !important; }
|
||
|
||
/* User-Select Quick-Access Classes */
|
||
.select-none { user-select: none; }
|
||
|
||
/* Position Quick-Access Classes */
|
||
.absolute { position: absolute; }
|
||
.relative { position: relative; }
|
||
.float-left { float: left; }
|
||
.float-right { float: right; }
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||
|
||
.pos-top { top: 0; }
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||
.pos-right { right: 0; }
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||
.pos-bottom { bottom: 0; }
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||
.pos-left { left: 0; }
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||
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||
.va-top { vertical-align: top; }
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||
.va-middle { vertical-align: middle; }
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||
.va-bottom { vertical-align: bottom; }
|
||
|
||
/* Transition Quick-Access Classes */
|
||
.trans-linear-quick {
|
||
transition: all 0.15s linear;
|
||
}
|
||
|
||
/* Background Quick-Access Classes */
|
||
.bg-gray { background: #cccccc; }
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||
.bg-white { background-color: white; }
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||
.bg-none { background: none; }
|
||
|
||
/* Border Quick-Access Classes */
|
||
.border-gray { border: 1px solid #cccccc; }
|
||
.tab-pane.border-gray { border: 1px solid #ddd; }
|
||
.no-border, .border-none { border: none !important; }
|
||
|
||
.bt-none { border-top: none !important; }
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||
.br-none { border-right: none !important; }
|
||
.bb-none { border-bottom: none !important; }
|
||
.bl-none { border-left: none !important; }
|
||
|
||
/* Border Radius Quick-Access Classes */
|
||
|
||
.round-t-sm, .round-tl-sm { border-top-left-radius: var(--sm-border-size); }
|
||
.round-t-sm, .round-tr-sm { border-top-right-radius: var(--sm-border-size); }
|
||
.round-b-sm, .round-bl-sm { border-bottom-left-radius: var(--sm-border-size); }
|
||
.round-b-sm, .round-br-sm { border-bottom-right-radius: var(--sm-border-size); }
|
||
.round-sm { border-radius: var(--sm-border-size); }
|
||
.round-fully { border-radius: 50%; }
|
||
|
||
/* Misc Quick-Access Classes */
|
||
.haikei {
|
||
position: relative;
|
||
background-image: url('https://dz2cdn1.dzone.com/themes/dz20/images/polygon-scatter-haikei-shapes.svg');
|
||
background-repeat: no-repeat;
|
||
background-size: cover;
|
||
}
|
||
|
||
.haikei:before {
|
||
content: '';
|
||
position:absolute;
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||
top: 0;
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||
left: 0;
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||
width: 100%;
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||
height: 100%;
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||
background-color: var(--color-event-background);
|
||
z-index: -1;
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||
}
|
||
|
||
.margin-auto-center {
|
||
display: block;
|
||
margin-left: auto;
|
||
margin-right: auto;
|
||
}
|
||
|
||
.flex-auto-center {
|
||
display: flex;
|
||
margin-left: auto;
|
||
margin-right: auto;
|
||
}
|
||
|
||
.mw-1100 { max-width: 1100px; }
|
||
.mw-1300 { max-width: 1300px; }
|
||
.full-width { width: 100%; }
|
||
|
||
.h-separator {
|
||
background-color: #cccccc;
|
||
height: 1px;
|
||
width: 100%;
|
||
margin: 5px auto;
|
||
}
|
||
|
||
.no-spacing {
|
||
margin: 0 !important;
|
||
padding: 0 !important;
|
||
}
|
||
|
||
.inherit-color { color: inherit; }
|
||
|
||
.inherit-decoration { text-decoration: inherit; }
|
||
|
||
/* Table Quick-Access Classes */
|
||
.table-border-gray tr:not(:last-child) {
|
||
border-bottom: 1px solid #cccccc;
|
||
}
|
||
|
||
table.full-width tr td:last-child {
|
||
width:100%;
|
||
}
|
||
|
||
/* Animation Quick-Access Classes */
|
||
@keyframes anim-spin {
|
||
0% { transform: rotate(0deg); }
|
||
100% { transform: rotate(359deg); }
|
||
}
|
||
|
||
.anim-spin {
|
||
display: inline-block;
|
||
animation: anim-spin 2s infinite linear;
|
||
}
|
||
|
||
.hov-pointer { cursor: pointer; }
|
||
.hov-brighten:hover { filter: brightness(1.2); }
|
||
.hov-darken:hover { filter: brightness(0.95); }
|
||
.hov-underline:hover { text-decoration: underline; }
|
||
|
||
/* Small tweaks related to components when using these styles */
|
||
.nav-tabs {
|
||
border-bottom: none;
|
||
}
|
||
.tab-content > .flex-column.active,
|
||
.tab-content > .flex-row.active {
|
||
display: flex;
|
||
}
|
||
|
||
/* Alpine components with x-cloak should not be visible by default until conditionals kick in */
|
||
[x-cloak] { display: none !important; }
|
||
</style>
|
||
<link rel="stylesheet" media="all" href="https://dz2cdn1.dzone.com/themes/dz20/ftl/alpine-components/modal.css">
|
||
<script defer src="https://dz2cdn1.dzone.com/themes/dz20/lib/alpinejs/3.13.2/cdn.min.js"></script>
|
||
<script>
|
||
document.addEventListener('alpine:init', () => {
|
||
Alpine.store('article', {
|
||
id: null,
|
||
engagement: {
|
||
open: false,
|
||
page: 1,
|
||
users: [],
|
||
additional: true,
|
||
loading: false,
|
||
initializing: false,
|
||
unauthorized: false,
|
||
enqueued: null,
|
||
dequeued: null,
|
||
|
||
reset() {
|
||
this.open = false;
|
||
this.page = 1;
|
||
this.users.splice(0);
|
||
this.additional = true;
|
||
this.loading = false;
|
||
this.initializing = false;
|
||
this.enqueued = null;
|
||
this.dequeued = null;
|
||
},
|
||
|
||
enqueue(user) {
|
||
this.dequeued = null;
|
||
this.enqueued = user;
|
||
},
|
||
|
||
dequeue(user) {
|
||
this.enqueued = null;
|
||
this.dequeued = user;
|
||
},
|
||
|
||
add(user) {
|
||
this.users.push(user);
|
||
this.enqueued = null;
|
||
},
|
||
|
||
remove(user) {
|
||
const index = this.users.findIndex(u => u.id === user.id);
|
||
|
||
if (index !== -1) {
|
||
this.users.splice(index, 1);
|
||
}
|
||
|
||
this.dequeued = null;
|
||
},
|
||
|
||
contains(user) {
|
||
return this.users.filter(u => u.id === user.id).length;
|
||
}
|
||
}
|
||
});
|
||
|
||
Alpine.effect(() => {
|
||
const store = Alpine.store('article');
|
||
if (!store.engagement.additional && store.engagement.enqueued) {
|
||
if (!store.engagement.contains(store.engagement.enqueued)) {
|
||
store.engagement.add(store.engagement.enqueued);
|
||
}
|
||
}
|
||
});
|
||
|
||
Alpine.effect(() => {
|
||
const store = Alpine.store('article');
|
||
if (store.engagement.page && store.engagement.dequeued) {
|
||
store.engagement.remove(store.engagement.dequeued);
|
||
}
|
||
})
|
||
});
|
||
</script></head>
|
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<body x-data>
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<div class="skybox skybox-closeBtn dz_skybox" data-gpt-slot="skybox" id="div-gpt-ad-1435246566686-99"></div>
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||
<div class="header-top">
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<div class="header-container">
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<div class="pull-left logo-container">
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<div class="logo">
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<a class="inner" href="/">
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<picture>
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<source srcset="https://dz2cdn1.dzone.com/themes/dz20/images/dz_logo_2021_cropped.webp" type="image/webp">
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<source srcset="https://dz2cdn1.dzone.com/themes/dz20/images/dz_logo_2021_cropped.png" type="image/png">
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<img src="https://dz2cdn1.dzone.com/themes/dz20/images/dz_logo_2021_cropped.png" width="181" height="56" alt="DZone">
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<div id="authenticated-block" class="logged-in">
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<div class="welcome-back">Thanks for visiting DZone today,</div>
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<button class="user-avatar">
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<span id="header-username" class="username"></span>
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<img id="header-avatar" src="" alt="user avatar">
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</button>
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<div id="user-dropdown" class="browse-user-menu">
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||
<div class="user-content">
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<a id="header-user-plug" href="#" class="user-description"></a>
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<a id="header-user-edit" href="#" class="edit-profile">Edit Profile</a>
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</div>
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<ul class="user-actions">
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<li id="first-user-action">
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<a id="header-dropdown-manage-email" href="#">Manage Email Subscriptions</a>
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<a href="/articles/how-to-submit-a-post-to-dzone?utm_source=DZone&utm_medium=user_dropdown&utm_campaign=how_to_post">
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</a>
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<a href="/articles/dzones-article-submission-guidelines">
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</ul>
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<a id="dropdown-view-profile" href="#" class="view-profile">View Profile</a>
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</div>
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<div class="post-content">
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<button id="post-button" class="post-content--button">
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<span class="post-class">Post</span>
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<i class="icon-plus"></i>
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</button>
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<div id="post-menu" class="posting-links">
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<div class="posting-links-menu">
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<ul>
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<li>
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<img src="https://dz2cdn1.dzone.com/themes/dz20/images/dz-postarticle.svg" width="15" height="18" style="width: 15px; height: 18px;">
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<a href="/content/article/post.html">Post an Article</a>
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</li>
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<li>
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<a id="drafts-link" href="#">Manage My Drafts</a>
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</li>
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</ul>
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</div>
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</div>
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<div class="dz-intro">Over 2 million developers have joined DZone.</div>
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<a href="/users/login.html">Log In</a>
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<a href="/static/registration.html">Join</a>
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</div>
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<a class="join-icon" href="/users/login.html" aria-label="User">
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<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide svg-user lucide-user-icon lucide-user">
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<path d="M19 21v-2a4 4 0 0 0-4-4H9a4 4 0 0 0-4 4v2"></path>
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<circle cx="12" cy="7" r="4"></circle>
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</svg>
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</a>
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</div>
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<script>
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document.addEventListener('alpine:init', () => {
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Alpine.data('searchDrawer', () => ({
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open: false,
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||
query: '',
|
||
minCharsMet: false,
|
||
|
||
toggleVisibility() {
|
||
this.open = !this.open;
|
||
if (this.open) {
|
||
this.$nextTick(() => {
|
||
this.$refs.query.focus();
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||
});
|
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}
|
||
},
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|
||
updateSearchValue() {
|
||
if (!this.query || !this.query.length || this.query.length < 3) {
|
||
this.minCharsMet = false;
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||
return;
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}
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||
this.minCharsMet = true;
|
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localStorage.setItem('ls.searchValue', this.query);
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||
},
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||
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searchSite() {
|
||
if (this.minCharsMet) {
|
||
window.location = '/search';
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}
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}
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}));
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});
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<div class="headerSearch" x-data="searchDrawer()" x-cloak>
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<button class="btn-search dropdown-toggle" x-on:click="toggleVisibility()" aria-label="Search">
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<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide svg-search lucide-search-icon lucide-search">
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<path d="m21 21-4.34-4.34"></path>
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<circle cx="11" cy="11" r="8"></circle>
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</svg>
|
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</button>
|
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<template x-teleport=".header-container">
|
||
<div id="search-drawer" x-show="open" x-on:click.outside="open = false" x-transition>
|
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<div class="search-input">
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<input type="text"
|
||
placeholder="Search"
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autofocus="autofocus"
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x-model="query"
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||
x-ref="query"
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x-on:input.change="updateSearchValue()"
|
||
x-on:keyup.enter="searchSite()"
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>
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<button x-on:click="searchSite()" x-bind:disabled="!minCharsMet">
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<span>Search</span>
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</button>
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</div>
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<div class="search-footer">
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Please enter at least three characters to search
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</div>
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</div>
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</template>
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</div>
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</div>
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</div> </div>
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<div class="header-bottom">
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<div class="header-bottom-container">
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<a class="resource-link" href="/refcardz">Refcards</a>
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<a class="resource-link" href="/trendreports">Trend Reports</a>
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<div class="resource-link link-menu">
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<a href="/events">Events</a>
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<a href="/events/video-library">Video Library</a>
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</div>
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</div>
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<nav class="header-menu-bar">
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<div class="header-menu resource-category">
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<a href="/refcardz">Refcards</a>
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</div>
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<div class="header-menu-separator resource-category-separator"></div>
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<div class="header-menu resource-category">
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<a href="/trendreports">Trend Reports</a>
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</div>
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<div class="header-menu-separator resource-category-separator"></div>
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<div class="header-menu resource-category no-bottom-radius" data-click-activation>
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<p class="menu-label">Events</p>
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<div class="header-menu-items">
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<div class="header-menu-columns">
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<a class="header-menu-item" href="/events">View Events</a>
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<a class="header-menu-item" href="/events/video-library">Video Library</a>
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</div>
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</div>
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</div>
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<div class="header-menu zone-menu" data-click-activation>
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<p class="menu-label">Zones <i class="icon-down-dir icon-closed"></i><i class="icon-right-dir icon-open"></i></p>
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<div class="header-menu-items">
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<div class="header-menu-columns">
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<div class="header-menu-column-item">
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<a class="header-menu-item" href="/culture-and-methodologies">Culture and Methodologies</a>
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<a class="header-menu-item" href="/career-development">Career Development</a>
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<a class="header-menu-item" href="/team-management">Team Management</a>
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<a class="header-menu-item" href="/data-engineering">Data Engineering</a>
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<a class="header-menu-item" href="/ai-ml">AI/ML</a>
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<a class="header-menu-item" href="/big-data">Big Data</a>
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<a class="header-menu-item" href="/data">Data</a>
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<a class="header-menu-item" href="/databases">Databases</a>
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<a class="header-menu-item" href="/iot">IoT</a>
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</div>
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<div class="header-menu-column-item">
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<a class="header-menu-item" href="/software-design-and-architecture">Software Design and Architecture</a>
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<a class="header-menu-item" href="/cloud-architecture">Cloud Architecture</a>
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<a class="header-menu-item" href="/integration">Integration</a>
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<a class="header-menu-item" href="/microservices">Microservices</a>
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<a class="header-menu-item" href="/performance">Performance</a>
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<a class="header-menu-item" href="/security">Security</a>
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</div>
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<div class="header-menu-column-item">
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<a class="header-menu-item" href="/coding">Coding</a>
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<a class="header-menu-item" href="/frameworks">Frameworks</a>
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</div>
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<div class="header-menu-column-item">
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<a class="header-menu-item" href="/testing-deployment-and-maintenance">Testing, Deployment, and Maintenance</a>
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<a class="header-menu-item" href="/deployment">Deployment</a>
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<a class="header-menu-item" href="/devops-and-cicd">DevOps and CI/CD</a>
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<a class="header-menu-item" href="/maintenance">Maintenance</a>
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<a class="header-menu-item" href="/monitoring-and-observability">Monitoring and Observability</a>
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<a class="header-menu-item" href="/testing-tools-and-frameworks">Testing, Tools, and Frameworks</a>
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</div>
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</div>
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</div>
|
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</div>
|
||
</div>
|
||
<div class="header-menu parent-category" tabindex="1">
|
||
<a href="/culture-and-methodologies">Culture and Methodologies</a>
|
||
<div class="header-menu-items">
|
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<a class="header-menu-item" href="/agile">Agile</a>
|
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<a class="header-menu-item" href="/career-development">Career Development</a>
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<a class="header-menu-item" href="/methodologies">Methodologies</a>
|
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<a class="header-menu-item" href="/team-management">Team Management</a>
|
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</div>
|
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</div>
|
||
<div class="header-menu-separator parent-category-separator"></div>
|
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<div class="header-menu parent-category" tabindex="2">
|
||
<a href="/data-engineering">Data Engineering</a>
|
||
<div class="header-menu-items">
|
||
<a class="header-menu-item" href="/ai-ml">AI/ML</a>
|
||
<a class="header-menu-item" href="/big-data">Big Data</a>
|
||
<a class="header-menu-item" href="/data">Data</a>
|
||
<a class="header-menu-item" href="/databases">Databases</a>
|
||
<a class="header-menu-item" href="/iot">IoT</a>
|
||
</div>
|
||
</div>
|
||
<div class="header-menu-separator parent-category-separator"></div>
|
||
<div class="header-menu parent-category" tabindex="3">
|
||
<a href="/software-design-and-architecture">Software Design and Architecture</a>
|
||
<div class="header-menu-items">
|
||
<a class="header-menu-item" href="/cloud-architecture">Cloud Architecture</a>
|
||
<a class="header-menu-item" href="/containers">Containers</a>
|
||
<a class="header-menu-item" href="/integration">Integration</a>
|
||
<a class="header-menu-item" href="/microservices">Microservices</a>
|
||
<a class="header-menu-item" href="/performance">Performance</a>
|
||
<a class="header-menu-item" href="/security">Security</a>
|
||
</div>
|
||
</div>
|
||
<div class="header-menu-separator parent-category-separator"></div>
|
||
<div class="header-menu parent-category" tabindex="4">
|
||
<a href="/coding">Coding</a>
|
||
<div class="header-menu-items">
|
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<a class="header-menu-item" href="/frameworks">Frameworks</a>
|
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<a class="header-menu-item" href="/java">Java</a>
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<a class="header-menu-item" href="/javascript">JavaScript</a>
|
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<a class="header-menu-item" href="/languages">Languages</a>
|
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<a class="header-menu-item" href="/tools">Tools</a>
|
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</div>
|
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</div>
|
||
<div class="header-menu-separator parent-category-separator"></div>
|
||
<div class="header-menu parent-category" tabindex="5">
|
||
<a href="/testing-deployment-and-maintenance">Testing, Deployment, and Maintenance</a>
|
||
<div class="header-menu-items">
|
||
<a class="header-menu-item" href="/deployment">Deployment</a>
|
||
<a class="header-menu-item" href="/devops-and-cicd">DevOps and CI/CD</a>
|
||
<a class="header-menu-item" href="/maintenance">Maintenance</a>
|
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<a class="header-menu-item" href="/monitoring-and-observability">Monitoring and Observability</a>
|
||
<a class="header-menu-item" href="/testing-tools-and-frameworks">Testing, Tools, and Frameworks</a>
|
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</div>
|
||
</div>
|
||
<div class="header-menu-separator parent-category-separator"></div>
|
||
<div class="header-menu parent-category sponsored" tabindex="6">
|
||
<a href="javascript:void(0)">Partner Zones</a>
|
||
<div class="header-menu-items sponsored">
|
||
<a class="header-menu-item" href="/hubs/build-ai-agents-that-are-ready-for-production/">Build AI Agents That Are Ready for Production</a>
|
||
</div>
|
||
</div>
|
||
<div class="header-menu-separator parent-category-separator"></div>
|
||
</nav>
|
||
</header>
|
||
|
||
<script>
|
||
const csrf = {
|
||
parameter: 'TH_CSRF',
|
||
header: 'X-TH-CSRF',
|
||
token: '-6185020111160919127'
|
||
}; // set csrf for auth-status script
|
||
</script>
|
||
<script>
|
||
addEventListener('DOMContentLoaded', function() {
|
||
handleRedirects();
|
||
handleMenus();
|
||
handleMobileMenuHeights();
|
||
handleGotoLinks();
|
||
});
|
||
|
||
function isHidden(element) {
|
||
try {
|
||
return window.getComputedStyle(element).display === 'none';
|
||
} catch (_) {
|
||
return false;
|
||
}
|
||
}
|
||
|
||
function getLink(element) {
|
||
if (element.hasAttribute('data-goto')) {
|
||
return element.getAttribute('data-goto');
|
||
}
|
||
return element.href;
|
||
}
|
||
|
||
function isLeftClick(event) {
|
||
if (event.altKey || event.shiftKey) {
|
||
return false;
|
||
} else if ('buttons' in event || 'which' in event) {
|
||
return event.buttons === 1 || event.which === 1;
|
||
} else {
|
||
return (event.button === 1 || (event.type === 'click'));
|
||
}
|
||
}
|
||
|
||
function handleRedirects() {
|
||
const redirections = [...document.querySelectorAll('[data-activate-menu]'), ...document.querySelectorAll('[data-click-target]')];
|
||
|
||
redirections.forEach(function(element) {
|
||
const menuSelector = element.getAttribute('data-activate-menu') || element.getAttribute('data-click-target');
|
||
const redirectingElement = document.querySelector(menuSelector);
|
||
if (redirectingElement) {
|
||
element.style.cursor = 'pointer';
|
||
const redirect = function(e) {
|
||
if (redirectingElement.hasAttribute('href') || redirectingElement.hasAttribute('data-goto')) {
|
||
if (redirectingElement.hasAttribute('data-new-window') || e.ctrlKey || e.metaKey) {
|
||
window.open(getLink(redirectingElement), '_blank');
|
||
} else {
|
||
window.open(getLink(redirectingElement), '_self');
|
||
}
|
||
} else {
|
||
const evt = new e.constructor(e.type, e);
|
||
redirectingElement.dispatchEvent(evt);
|
||
}
|
||
};
|
||
element.addEventListener('mouseup', redirect);
|
||
element.addEventListener('mousedown', (e) => e.preventDefault());
|
||
element.addEventListener('click', (e) => e.preventDefault());
|
||
}
|
||
});
|
||
}
|
||
|
||
function handleMenus() {
|
||
const menuElements = [
|
||
...document.querySelectorAll('.header-menu > a'),
|
||
...document.querySelectorAll('.header-menu > p.menu-label')
|
||
];
|
||
|
||
let scrollYMemory = -1;
|
||
|
||
function scrollToMemory() {
|
||
if (scrollYMemory !== -1) {
|
||
setTimeout(function() {
|
||
window.scrollTo(0, scrollYMemory);
|
||
scrollYMemory = -1;
|
||
}, 10);
|
||
}
|
||
}
|
||
|
||
function hideMenus() {
|
||
// unfocus menus & items, and set the menus to non-visible.
|
||
menuElements.forEach(function (element) {
|
||
element.blur();
|
||
element.parentElement.blur();
|
||
element.parentElement.classList.remove('menu-opened');
|
||
const menuItems = element.parentElement.querySelector('.header-menu-items');
|
||
if (menuItems) {
|
||
menuItems.style.display = 'none';
|
||
}
|
||
});
|
||
const wasHidden = document.body.style.overflowY === 'hidden';
|
||
document.body.style.overflowY = 'auto';
|
||
if (wasHidden) {
|
||
scrollToMemory();
|
||
}
|
||
}
|
||
|
||
function isEventOutsideMenu(e) {
|
||
return e.target.closest && !e.target.closest('.header-menu') && !e.target.closest('[data-activate-menu]');
|
||
}
|
||
|
||
// Handle mobile menu toggling
|
||
menuElements.forEach(function(element) {
|
||
const menu = element.parentElement;
|
||
const headerItems = menu.querySelector('.header-menu-items');
|
||
const menuEntries = headerItems ? headerItems.querySelectorAll('.header-menu-item') : [];
|
||
|
||
const focus = function() {
|
||
menu.focus();
|
||
menu.classList.add('menu-opened');
|
||
if (headerItems) {
|
||
headerItems.style.display = 'block';
|
||
}
|
||
if (menu.classList.contains('zone-menu')) {
|
||
scrollYMemory = window.scrollY;
|
||
document.body.style.overflowY = 'hidden';
|
||
} else {
|
||
scrollYMemory = -1;
|
||
}
|
||
};
|
||
|
||
const unfocus = function() {
|
||
menu.blur();
|
||
menu.classList.remove('menu-opened');
|
||
if (headerItems) {
|
||
headerItems.style.display = 'none';
|
||
}
|
||
const wasHidden = document.body.style.overflowY === 'hidden';
|
||
document.body.style.overflowY = 'auto';
|
||
if (wasHidden) {
|
||
scrollToMemory();
|
||
}
|
||
};
|
||
|
||
const toggleMenuVisibility = function(e) {
|
||
if ((e.type === 'click' || e.type === 'mouseup') && !isLeftClick(e)) {
|
||
e.preventDefault();
|
||
return;
|
||
}
|
||
const hidden = isHidden(headerItems);
|
||
if (menu.hasAttribute('data-click-activation')) { // handle click activated toggling
|
||
if (hidden) {
|
||
hideMenus(); // hide other open menus first
|
||
focus();
|
||
} else {
|
||
unfocus();
|
||
}
|
||
e.preventDefault();
|
||
} else if (hidden) {
|
||
hideMenus(); // hide other open menus first
|
||
focus();
|
||
e.preventDefault(); // prevent 'click' event from firing when menu is hidden
|
||
}
|
||
};
|
||
|
||
element.addEventListener('touchend', toggleMenuVisibility);
|
||
element.addEventListener('mouseup', toggleMenuVisibility);
|
||
|
||
// Add hover events to non-click-activated menus, even though CSS should cover it.
|
||
if (!menu.hasAttribute('data-click-activation')) {
|
||
menu.addEventListener('mouseover', function () {
|
||
hideMenus(); // hide other open menus first
|
||
focus();
|
||
});
|
||
|
||
menu.addEventListener('mouseout', function (e) {
|
||
if (isEventOutsideMenu(e)) {
|
||
unfocus();
|
||
}
|
||
});
|
||
}
|
||
|
||
// Hide menu when child is clicked
|
||
menuEntries.forEach(function(menuEntry) {
|
||
const linkToItem = function(e) {
|
||
if (e.type === 'mousedown' || e.type === 'click') {
|
||
e.preventDefault();
|
||
return;
|
||
}
|
||
if (e.type === 'mouseup' && !isLeftClick(e)) {
|
||
e.preventDefault();
|
||
return;
|
||
}
|
||
window.open(getLink(menuEntry), (e.ctrlKey || e.metaKey) ? '_blank' : (menuEntry.target || '_self'));
|
||
unfocus();
|
||
e.preventDefault();
|
||
};
|
||
|
||
const linkToMobileItem = function(e) {
|
||
if (e.type === 'touchstart') {
|
||
menuEntry.setAttribute('data-touchmove', false);
|
||
} else if (e.type === 'touchmove') {
|
||
menuEntry.setAttribute('data-touchmove', true);
|
||
} else if (e.type === 'touchend' && (!menuEntry.hasAttribute('data-touchmove') || menuEntry.getAttribute('data-touchmove').toLowerCase() === 'false')) {
|
||
window.open(getLink(menuEntry), (e.ctrlKey || e.metaKey) ? '_blank' : (menuEntry.target || '_self'));
|
||
unfocus();
|
||
e.preventDefault();
|
||
}
|
||
};
|
||
|
||
menuEntry.addEventListener('mousedown', linkToItem);
|
||
menuEntry.addEventListener('mouseup', linkToItem);
|
||
menuEntry.addEventListener('click', linkToItem);
|
||
menuEntry.addEventListener('touchstart', linkToMobileItem);
|
||
menuEntry.addEventListener('touchmove', linkToMobileItem);
|
||
menuEntry.addEventListener('touchend', linkToMobileItem);
|
||
});
|
||
});
|
||
|
||
function hideIfNonMenuBounds(e) {
|
||
if (isEventOutsideMenu(e)) {
|
||
hideMenus();
|
||
}
|
||
}
|
||
|
||
addEventListener('mousemove', hideIfNonMenuBounds);
|
||
addEventListener('touchend', hideIfNonMenuBounds);
|
||
addEventListener('mouseup', hideIfNonMenuBounds);
|
||
addEventListener('mousedown', hideIfNonMenuBounds);
|
||
addEventListener('click', hideIfNonMenuBounds);
|
||
}
|
||
|
||
function handleMobileMenuHeights() {
|
||
function setAppHeight() {
|
||
document.documentElement.style.setProperty('--app-height', window.innerHeight + 'px');
|
||
}
|
||
|
||
addEventListener('resize', function() {
|
||
setAppHeight();
|
||
});
|
||
|
||
setAppHeight();
|
||
}
|
||
|
||
function handleGotoLinks() {
|
||
// Add anchor mimicking to elements with data-goto attributes
|
||
// This addresses SEO concerns of linking to noindex pages by allowing JS to handle the URL
|
||
const anchorElements = document.querySelectorAll('*[data-goto]');
|
||
anchorElements.forEach((anchorElement) => {
|
||
anchorElement.addEventListener('mouseover', () => {
|
||
anchorElement.style.cursor = 'pointer';
|
||
anchorElement.style.textDecoration = 'underline';
|
||
});
|
||
anchorElement.addEventListener('mouseout', () => {
|
||
anchorElement.style.cursor = 'unset';
|
||
anchorElement.style.textDecoration = 'unset';
|
||
});
|
||
anchorElement.addEventListener('mouseup', (e) => {
|
||
e.preventDefault();
|
||
});
|
||
anchorElement.addEventListener('mousedown', (e) => {
|
||
e.preventDefault();
|
||
});
|
||
anchorElement.addEventListener('click', (e) => {
|
||
e.preventDefault();
|
||
const anchorHref = anchorElement.getAttribute('data-goto');
|
||
if (anchorElement.hasAttribute('data-new-window') || e.ctrlKey || e.metaKey) {
|
||
window.open(anchorHref, '_blank');
|
||
} else {
|
||
window.open(anchorHref, '_self');
|
||
}
|
||
});
|
||
});
|
||
}
|
||
</script><script>
|
||
const authenticatedBlock = document.querySelector('#authenticated-block');
|
||
const unauthenticatedBlock = document.querySelector('#unauthenticated-block');
|
||
|
||
let authenticated = {
|
||
isAuthenticated: false,
|
||
isAdmin: false,
|
||
user: {
|
||
id: null,
|
||
name: null,
|
||
url: null,
|
||
profileImage: null,
|
||
}
|
||
};
|
||
|
||
fetch('/services/internal/data/articles-getAuthenticationStatus', {
|
||
headers: {
|
||
'Accept': 'application/json'
|
||
}
|
||
})
|
||
.then(function (result) {
|
||
return result.json()
|
||
})
|
||
.then(function (result) {
|
||
const res = result.result.data
|
||
if (!res.authenticated) {
|
||
unauthenticatedBlock.classList.add('shown')
|
||
} else {
|
||
authenticated.user.id = res.id;
|
||
authenticated.user.name = res.realName;
|
||
authenticated.user.url = res.profileUrl;
|
||
authenticated.user.profileImage = res.avatar;
|
||
authenticated.user.jobTitle = res.jobTitle;
|
||
authenticated.user.companyName = res.companyName;
|
||
|
||
bindProps('#header-username', null, null, (res.firstName || res.username), null)
|
||
bindProps('#header-avatar', null, res.avatar, null, null)
|
||
bindProps('#header-user-plug', res.profileUrl, null, res.realName, null)
|
||
bindProps('#header-user-edit', '/users/' + res.id + '/edit.html', null, null, null)
|
||
bindProps('#header-dropdown-manage-email', '/newsletters/' + res.id + '/manage.html', null, null, null)
|
||
bindProps('#dropdown-view-profile', res.profileUrl, null, null, null)
|
||
bindProps('#drafts-link', '/users/' + res.id + '/drafts.html', null, null, null)
|
||
|
||
if (res.isAdmin) {
|
||
// Construct backwards so the #after call places elements in the correct order
|
||
const firstUserAction = document.querySelector('#first-user-action')
|
||
const adminConsoleItem = document.createElement('li')
|
||
const adminConsoleLink = createLink('/dzone/staff/index.html', 'Admin Console')
|
||
adminConsoleItem.appendChild(adminConsoleLink)
|
||
firstUserAction.after(adminConsoleItem)
|
||
|
||
const moderationItem = document.createElement('li')
|
||
const moderationLink = createLink('/moderation/list.html', 'Moderation')
|
||
moderationItem.appendChild(moderationLink)
|
||
firstUserAction.after(moderationItem)
|
||
|
||
const bountyModerationItem = document.createElement('li')
|
||
const bountyModerationLink = createLink('/moderation/bounties', 'Bounty Moderation')
|
||
bountyModerationItem.appendChild(bountyModerationLink)
|
||
firstUserAction.after(bountyModerationItem)
|
||
}
|
||
|
||
authenticated.isAuthenticated = res.authenticated;
|
||
authenticated.isAdmin = res.isAdmin;
|
||
|
||
authenticatedBlock.classList.add('shown')
|
||
}
|
||
}).catch(function (result) {
|
||
console.error(result)
|
||
})
|
||
|
||
/**
|
||
* Binds different properties to the selected element.
|
||
*
|
||
* @param selector - Selector to select the element
|
||
* @param href - href attribute value
|
||
* @param src - src attribute value
|
||
* @param innerHTML - innerHTML property value
|
||
* @param innerText - innerText property value
|
||
*/
|
||
function bindProps(selector, href, src, innerHTML, innerText) {
|
||
const element = document.querySelector(selector)
|
||
if (element) {
|
||
if (href) element.href = href
|
||
if (src) element.src = src
|
||
if (innerHTML) element.innerHTML = innerHTML
|
||
if (innerText) element.innerText = innerText
|
||
}
|
||
}
|
||
|
||
/**
|
||
* Creates a new link element.
|
||
*
|
||
* @param href - href attribute value
|
||
* @param innerText - innerText property value
|
||
* @returns {HTMLAnchorElement} The generated link element
|
||
*/
|
||
function createLink(href, innerText) {
|
||
const link = document.createElement('a')
|
||
link.href = href
|
||
link.innerText = innerText
|
||
|
||
return link
|
||
}
|
||
</script><script>
|
||
const userHeader = document.querySelector('#user-header')
|
||
const userDropdown = document.querySelector('#user-dropdown')
|
||
const postDropdown = document.querySelector('#post-button')
|
||
const postMenu = document.querySelector('#post-menu')
|
||
|
||
let userDropdownOpen = false
|
||
let postDropdownOpen = false
|
||
|
||
document.addEventListener('click', function(event) {
|
||
if (postDropdown && postDropdown.contains(event.target)) {
|
||
setUserDropdown(false)
|
||
setPostDropdown(!postDropdownOpen)
|
||
} else if (userHeader && userHeader.contains(event.target)) {
|
||
setPostDropdown(false)
|
||
setUserDropdown(!userDropdownOpen)
|
||
} else {
|
||
setUserDropdown(false)
|
||
setPostDropdown(false)
|
||
}
|
||
})
|
||
|
||
function setUserDropdown(value) {
|
||
userDropdownOpen = value
|
||
if (userDropdownOpen) {
|
||
if (userDropdown) {
|
||
userDropdown.classList.add('open')
|
||
}
|
||
} else {
|
||
if (userDropdown) {
|
||
userDropdown.classList.remove('open')
|
||
}
|
||
}
|
||
}
|
||
|
||
function setPostDropdown(value) {
|
||
postDropdownOpen = value
|
||
if (postDropdownOpen) {
|
||
if (postMenu) {
|
||
postMenu.classList.add('open')
|
||
}
|
||
} else {
|
||
if (postMenu) {
|
||
postMenu.classList.remove('open')
|
||
}
|
||
}
|
||
}
|
||
</script><script>
|
||
document.addEventListener("alpine:init", () => {
|
||
Alpine.store('global', {
|
||
executeHttp(path, body, method) {
|
||
const options = {
|
||
method: method,
|
||
headers: {
|
||
[csrf.header]: csrf.token
|
||
}
|
||
};
|
||
|
||
if (method !== 'GET') {
|
||
options.body = JSON.stringify(body);
|
||
options.headers = {
|
||
...options.headers,
|
||
'Content-Type': 'application/json; charset=UTF-8'
|
||
};
|
||
}
|
||
|
||
return new Promise((resolve, reject) => {
|
||
fetch(path, options)
|
||
.then(res => {
|
||
if (!res.ok) {
|
||
reject(res);
|
||
} else {
|
||
resolve(res);
|
||
}
|
||
})
|
||
.catch(err => reject(err));
|
||
});
|
||
},
|
||
getFromService(path) {
|
||
return this.executeHttp(path, null, 'GET');
|
||
},
|
||
postToService(path, body = {}) {
|
||
return this.executeHttp(path, body, 'POST');
|
||
},
|
||
putToService(path, body = {}) {
|
||
return this.executeHttp(path, body, 'PUT');
|
||
}
|
||
});
|
||
});
|
||
</script>
|
||
<link rel="stylesheet" media="all" href="https://dz2cdn1.dzone.com/themes/dz20/ftl/colors.css">
|
||
<link rel="stylesheet" media="all" href="https://dz2cdn1.dzone.com/themes/dz20/ftl/article/styles.css">
|
||
|
||
|
||
|
||
|
||
|
||
<div id="body-container">
|
||
<div id="announcement-container-outer">
|
||
<div id="announcement-previous">
|
||
<i class="icon-angle-left"></i>
|
||
</div>
|
||
<div id="announcement-next">
|
||
<i class="icon-angle-right"></i>
|
||
</div>
|
||
<div id="announcement-container">
|
||
<div class="announcement announcement-count-1"
|
||
data-position="1">
|
||
<div class="body"><p><strong>Could your team report a vulnerability within 24 hours? </strong>Find out on September 23.</p></div>
|
||
<div class="spacer"></div>
|
||
<a href="https://cvent.me/O32zXR?utm_source=Announcements&utm_medium=DzoneWeb&utm_campaign=QtGroup-0923" target="_blank">
|
||
<button>Assess Your CRA Readiness</button>
|
||
</a>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<script type="text/javascript" async>
|
||
(function() {
|
||
let announcementPosition = 1;
|
||
let minAnnouncementPosition = -1;
|
||
let maxAnnouncementPosition = -1;
|
||
|
||
const announcementPrevBtn = document.querySelector('#announcement-previous');
|
||
const announcementNextBtn = document.querySelector('#announcement-next');
|
||
|
||
function withAnnouncements(callback) {
|
||
const announcements = document.querySelectorAll('#announcement-container .announcement');
|
||
for (let announcement of announcements) {
|
||
callback(announcement);
|
||
}
|
||
}
|
||
|
||
function initAnnouncementVars() {
|
||
document.querySelector(':root').style.setProperty('--mobile-announcement-separator-width', '1px');
|
||
withAnnouncements(function(announcement) {
|
||
const pos = parseInt(announcement.getAttribute('data-position'));
|
||
minAnnouncementPosition = minAnnouncementPosition === -1 ? pos : Math.min(minAnnouncementPosition, pos);
|
||
maxAnnouncementPosition = maxAnnouncementPosition === -1 ? pos : Math.max(maxAnnouncementPosition, pos);
|
||
});
|
||
|
||
if (document.querySelector('.announcementBarContainer')) {
|
||
document.querySelector(':root').style.setProperty('--body-top-padding', '0');
|
||
}
|
||
|
||
if (maxAnnouncementPosition <= 0) {
|
||
document.querySelector(':root').style.setProperty('--body-top-padding', '0');
|
||
}
|
||
|
||
sizeToFullWhenOneEntryOnMobile();
|
||
}
|
||
|
||
function setAnnouncementPosition(position) {
|
||
if (window.outerWidth >= 890) {
|
||
return; // we do not need to change the position, since we can display everything on desktop.
|
||
}
|
||
|
||
// Make the announcement cyclical
|
||
if (minAnnouncementPosition !== -1 && maxAnnouncementPosition !== -1) {
|
||
if (position > maxAnnouncementPosition) {
|
||
position = minAnnouncementPosition; // overflow to the first announcement
|
||
} else if (position < minAnnouncementPosition) {
|
||
position = maxAnnouncementPosition; // underflow to the last announcement
|
||
}
|
||
announcementPosition = position;
|
||
}
|
||
|
||
const shownAnnouncements = [];
|
||
|
||
let reverseFlex = false; // should only be true when the first and last items are showing
|
||
|
||
// Now that the position is valid, apply the transforms
|
||
withAnnouncements(function(announcement) {
|
||
const pos = parseInt(announcement.getAttribute('data-position'));
|
||
const isWrapped = position === maxAnnouncementPosition && pos === minAnnouncementPosition; // showing first + last at same time
|
||
const doesNextQualify = window.outerWidth >= 500 && (pos === position + 1 || isWrapped);
|
||
|
||
if (pos === position || doesNextQualify) {
|
||
shownAnnouncements.push(announcement);
|
||
} else {
|
||
announcement.style.display = 'none';
|
||
}
|
||
|
||
announcement.style.opacity = 0.0;
|
||
|
||
if (isWrapped) {
|
||
reverseFlex = true;
|
||
}
|
||
});
|
||
|
||
for (let announcement of shownAnnouncements) {
|
||
announcement.style.display = 'flex';
|
||
announcement.style.opacity = 1.0;
|
||
}
|
||
|
||
const announcementContainer = document.querySelector('#announcement-container');
|
||
if (announcementContainer) {
|
||
announcementContainer.style.flexDirection = reverseFlex ? 'row-reverse' : 'row';
|
||
}
|
||
}
|
||
|
||
function resetAnnouncements() {
|
||
announcementPosition = 1;
|
||
const announcementContainer = document.querySelector('#announcement-container');
|
||
if (announcementContainer) {
|
||
announcementContainer.style.flexDirection = 'row';
|
||
}
|
||
withAnnouncements(function(announcement) {
|
||
announcement.style.opacity = 1.0;
|
||
announcement.style.display = 'flex';
|
||
});
|
||
}
|
||
|
||
function sizeToFullWhenOneEntryOnMobile() {
|
||
if (maxAnnouncementPosition <= 2 && announcementPrevBtn && announcementNextBtn) {
|
||
announcementPrevBtn.style.display = maxAnnouncementPosition === 2 && window.outerWidth < 500 ? 'flex' : 'none';
|
||
announcementNextBtn.style.display = maxAnnouncementPosition === 2 && window.outerWidth < 500 ? 'flex' : 'none';
|
||
}
|
||
if (maxAnnouncementPosition === 1 && window.outerWidth >= 500 && window.outerWidth < 890) {
|
||
document.querySelector(':root').style.setProperty('--mobile-announcement-separator-width', '0');
|
||
document.querySelector('#announcement-container .announcement').style.maxWidth = '100%';
|
||
}
|
||
}
|
||
|
||
function resetAnnouncementsOnResize() {
|
||
announcementPosition = 1; // we want to reset the announcement position every resize
|
||
if (window.outerWidth >= 890) { // 4 announcements can be shown (215px * 4) + separator padding
|
||
resetAnnouncements();
|
||
} else { // if we're still on mobile, set the position to normalize things after resize
|
||
setAnnouncementPosition(announcementPosition);
|
||
sizeToFullWhenOneEntryOnMobile();
|
||
}
|
||
}
|
||
|
||
window.addEventListener('resize', resetAnnouncementsOnResize);
|
||
|
||
initAnnouncementVars();
|
||
setAnnouncementPosition(announcementPosition); // sets the min & max positions as well as initializes transforms
|
||
|
||
if (announcementPrevBtn) {
|
||
announcementPrevBtn.onclick = function() {
|
||
setAnnouncementPosition(announcementPosition - 1);
|
||
};
|
||
}
|
||
|
||
if (announcementNextBtn) {
|
||
announcementNextBtn.onclick = function() {
|
||
setAnnouncementPosition(announcementPosition + 1);
|
||
};
|
||
}
|
||
})(); </script>
|
||
<div id="ftl-article" class="trending-article-body">
|
||
<aside class="trending-sidebar" aria-labelledby="related-sidebar-heading">
|
||
<div class="trending">
|
||
<h2 id="related-sidebar-heading">Related</h2>
|
||
<div class="trending-separator"></div>
|
||
<ul>
|
||
<li class="item">
|
||
<a href="/articles/scaling-sre-teams" class="related-link">Scaling SRE Teams: The Challenges and How To Build a Successful Scaling Framework</a>
|
||
</li>
|
||
<li class="item">
|
||
<a href="/articles/angular-agentic-ui" class="related-link">Angular Apps Don’t Need Another Chatbot: Building Agentic UI Workflows With TypeScript</a>
|
||
</li>
|
||
<li class="item">
|
||
<a href="/articles/ai-agent-frameworks" class="related-link">A Field Guide to AI Agent Frameworks</a>
|
||
</li>
|
||
<li class="item">
|
||
<a href="/articles/building-metadata-driven-quality-framework" class="related-link">Stop Hardcoding Database Checks: Building a Metadata-Driven Data Quality Framework</a>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
</aside>
|
||
<div class="container-fluid body trending-article-fluid">
|
||
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"articleBody": "Here's a problem I've seen happen far too often: your recommendation system is functioning, spitting out results in milliseconds, and meeting all its infrastructure SLAs. Everything is looking rosy in the dashboard world. Yet engagement has plummeted by 40% because your model has been pointless for several weeks. On behalf of your traditional error budget? You're golden. According to your product team? The system is broken. ML systems fail in ways that were not accounted for in classical SRE practices. A model does not 'go down'; it gradually deteriorates. Data pipelines can be 'working' while providing garbage to the model. And you won't even realize this until users start to complain or, worst, quietly depart. The past few years spent breaking and fixing ML systems have taught me that we need a paradigm shift in our error budget. Here's how it works. Understanding the Limitations of Conventional Error Budgets The challenge here is that \"reliability\" in ML does not live on a one-dimensional spectrum. Your API could be functioning correctly even if your model is not working. Your model could be working correctly even if your data pipeline is providing stale features to your model. You could be doing great on your aggregate numbers even if you're treating some users unfairly. What I've found is that you need to break down four different error budgets. Mapping These to Actual Error Budgets Before delving into each dimension, I must clarify the application of these to conventional SRE error budgets — not merely health checks: For each dimension, you require: • SLI (service level indicator): What you're measuring • SLO (service level objective): Your target over time • Error budget: How much you can miss the SLO before you take action Here's what model quality means with concrete examples: SLI: Accuracy of the model compared with the baseline, hourly SLO: Accuracy ≥ 92% of Baseline over the rolling 7 days Error budget: 8% allowable error in 7 days Burn rate: Monitor hourly; warn for burning above 10% of budget daily The main difference versus an error budget is that you're measuring degradation relative to a known-good state as opposed to just measuring success or failure. The math is exactly the same in both cases — a time budget that gets spent if you don't meet your SLO. Now, let's consider every dimension one by one: 1. Infrastructure Error Budget These are your standard SRE metrics: uptime, latency, and success rate of requests. It's old news, but you should have this as your baseline. What I monitor: 99.95% availability, latency of sub-150ms at p95, 99.9% success rate 2. Model Quality Error Budget This is where it gets fascinating. You must specify at what point you are willing to let the degradation of your model become noisy. What I track: • Model accuracy vs baseline accuracy (typically up to 8% loss) • Percentage of low-confidence predictions • Distribution of feature drift via statistical tests Here's how I can determine degradation: Python # Compare Current Performance with Your Personal Benchmark accuracy_degradation = (baseline_accuracy - current_accuracy) / baseline_accuracy budget_burn_rate = accuracy_degradation / acceptable_degradation Real example: Accuracy decreased from 95% to 93%, my threshold is 8% As for drift detection, I employ the Kolmogorov-Smirnov test: Python # Verify distribution of features has changed from scipy.stats import ks_2samp statistic, p_value = ks_2samp(baseline_features, current_features) drift_alert = p_value < 0.05 One thing that bit me: Tie your model accuracy metrics to business metrics. Rather than accuracy percentages, track something your PM cares about — for example, \"click-through rate stays within 95% of baseline.\" 3. Data Quality Error Budget Garbage in, garbage out. However, the ML system \"garbage\" needs a different definition. What matters: • Feature completeness score (my target is 99%+) • Feature freshness degree (how many features are stale?) • Schema violations Simple quality check: Python def simple_quality_check(features): missing_rate = missing_features / total_features stale_rate = stale_features / total_features data_quality_score = min(1 - missing_rate, 1 - stale_rate) meets_sli = data_quality_score > 0.99 Traditional data pipelines only cared about having a correct schema. When working with machine learning, you also want to ensure that your data features are fresh enough and that your distributions look fairly regular. I've been burned before working on pipelines that \"worked\" but passed day-old data, making our model irrelevant. 4. Fairness Error Budget In your case, fairness can be either desirable or mandatory. Regardless, it should be tracked. What I monitor: • Differences in accuracy across demographic groups (this is under 5%) • False positive rate parity across segments To calculate disparate impact: Python # Determine disparate impact group_A_rate = predictions[group == 'A'].mean() group_B_rate = predictions[group == 'B'].mean() disparity = abs(group_A_rate - group_B_rate) violation = disparity > 0.05 # flag if over 5% There is no such dimension in traditional SRE because a traditional system is not involved in people's decision-making. However, as soon as your machine learning system starts approving loans or ranking candidates for jobs, you want to determine whether your system is treating people fairly. Critical Caveats Fairness metrics are extremely domain-specific and complex from a legal standpoint. The metrics that I am presenting here are only examples, and demographic parity is not necessarily a good thing for every problem you want to solve. Before using fairness budgets: Discuss with lawyers the way in which fairness may be considered in your regulatory environmentCoordinate with the product and policy teams on identifying the acceptable tradeoffsReflect on whether you have the right to maintain, process, or use sensitive attributes for monitoring purposesDo not use simplistic parity checks as the sole indicators of fairness In regulated industries such as finance, healthcare, or hiring, you require knowledge that goes beyond the capabilities of any framework. How to Actually Implement This Step 1: Determine How Reliability Applies in Your Business Don't begin with metrics in mind. Begin with conversations instead. \"What is a broken model in the eyes of my PM?\" \"What will make my users grumble?\" For an ML-driven search functionality, you can choose: Infrastructure: Less than 200 ms (p95)Model quality: Relevance scores greater than 0.85 relative to human assessorsData quality: Less than 1% of queries missing critical featuresFairness: Search diversity preserved when considering different user categories Step 2: Establish Your Baseline Run your system in a stable state for 30 days. Observe what \"good\" looks like. Python # Calculate your baseline during a stable period baseline = { 'accuracy': np.percentile(stable_metrics['accuracy'], 50), 'p95_latency': np.percentile(stable_metrics['latency'], 95), 'drift_threshold': calculate_drift_threshold(stable_features) } This becomes your north star. All else shall be measured from that. Step 3: Define Ownership This is crucial. Each dimension must have a \"clear owner\" to make decisions and take actions: Infrastructure budget → SRE owns: • Right to suspend deployments • Authority to reverse modifications • Infrastructure scaling authority Model quality budget → ML engineering owns: • Authority for triggering retraining • Authority to roll back to previous model version • Power to increase monitoring frequency Data quality budget → data engineering owns: • Power to halt data pipelines • Authority to enable fallback data sources • Right to disregard upstream data Fairness budget → ML + product + legal own together: • Needs a multi-stakeholder decision for any actions • Product evaluates business impact • Legal specifies compliance requirements • ML applies technical solutions If the budget constraints are conflicting, such that model quality is satisfactory, but fairness is violated, then the more constraining budget prevails. If you have depleted your fairness budget, you cannot just rely on your predictions for satisfactory accuracy. Step 4: Monitor Everything Establish dashboards to measure all four key dimensions. Here's how I calculate the composite health factors: Python # Current health across dimensions dimensions = { 'infrastructure': 0.95, # meeting 95% of SLO 'model_quality': 0.88, # at 88% of baseline 'data_quality': 0.98, 'fairness': 0.96 } # Weight them according to what is important to your business weights = { 'infrastructure': 0.3, 'model_quality': 0.35, 'data_quality': 0.2, 'fairness': 0.15 } composite_score = sum(dimensions[d] * weights[d] for d in dimensions) Critical note: The composite score is solely for executive visibility. Hard enforcement always happens on a per-dimension basis. Having a 90% composite score does not supersede a violation in any dimension. You are in violation if you blow your fairness budget. Step 5: Know What to Do When Budgets Blow Up This list should be recorded prior to having a situation on your hands: Infrastructure budget spent out: Stop deployments, undo changes made, see if scale is requiredModel quality budget used up: Kick off the retraining process, think about reverting to the former model version, and look at what changed in your datasetData Quality budget exhausted: Check your upstream data sources, validate your ETL pipeline, turn on feature fallbacks if you have themFairness budget used up: If it's bad, then simply stop making predictions for those subgroups. Don't release it to society until you figure out where you introduced unfair bias and retrain. A Real Example: Fraud Detection Let me illustrate what I mean with a system for preventing fraud that I built for a fintech company. Our error budgets: Infrastructure: 99.99% uptime, under 100ms at p95Model quality: Precision above 95%, Recall above 90%, False Positive Rate below 2%Data quality: +99.5% feature completion rate, <1% stale featuresFairness: FPR differences across merchant types <3% Here's what our code for monitoring looked like: Python # Validating the health of each batch of predictions made def check_fraud_detection_health(predictions, features, ground_truth): # Did model quality degrade? current_precision = precision_score(ground_truth, predictions) precision_violation = (baseline - current_precision) / baseline > 0.02 # Are features getting stale? stale_rate = features[features['age_hours'] > 24].shape[0] / len(features) data_violation = stale_rate > 0.01 # Fairness issues regarding various merchants? fprs = calculate_fpr_by_category(predictions, ground_truth) fairness_violation = max(fprs.values()) - min(fprs.values()) > 0.03 return any([precision_violation, data_violation, fairness_violation]) The \"interesting\" part: All these dimensions are actually tested in every prediction batch. It helps you detect issues early, as data quality problems could become evident before affecting model performance. A Few Things I've Learned Use Rolling Windows Where Time-Based Budgets Are Required Monthly budgets aren't really effective in ML either. You may have a bad week when you're retraining your model, but you can't waste the rest of the budget. I use 7-day rolling windows instead — still time budgets, but with a sliding window. Python from collections import deque # Measurements deque with maxlen of 7 days * 24 hours measurements = deque(maxlen=168) measurements.append({'timestamp': now, 'accuracy': current_accuracy}) avg_accuracy = sum([m['accuracy'] for m in measurements]) / len(measurements) budget_ok = avg_accuracy >= target_accuracy This provides some buffer for recovering from transient problems without having to call bankruptcy for the month. You're still measuring reliability over time (the point of error budgets), but the window slides smoothly rather than restarting each month. Budget According to What Is Happening In a large product rollout, I'll cut model quality budgets (can't have the model shaming us during peak traffic) while relaxing latency requirements slightly. It's fine to adjust these based on context, just be sure to record the reasoning behind adjustments as they happen. Be Alert for Cascading Failures \"Garbage in, garbage out\" applies here, too: bad data input leads to bad model output, which, in turn, results in more attempts and fallbacks, thus more load on the infrastructure. It is where having budgets per dimension comes in handy, as it allows you to zero in on where the problem actually occurred. Wrapping Up Conventional error budgets account for failures in infrastructure, such as servers becoming unavailable and requests timing out. They fail to account, however, for failure in ML, which occurs in terms of model drift, pipelines with stale features, and biased predictions in terms of user segments. This framework identifies these failures early. By monitoring the degradation of model quality with time, you address the issue before it affects users. By monitoring the freshness of the data, you identify the pipeline failures before their impact affects your predictions. By monitoring fairness, you identify bias before it turns into a compliance issue. The actual gains in reliability come from the following three sources: Earlier detection: You detect degradation trends before outagesRoot cause clarity: When quality goes down, you know if it's the infrastructure or the quality of the dataClear accountability: Every factor has a clear owner who has clear action power You want to start with the budget on infrastructure and the quality of models. Get familiar with tracking the baseline and calculating the burn rate. Once you're comfortable with that, you can integrate the data quality tracking. Fairness tracking is what you want to do last. It's the most complex aspect of fairness, and it's the most dependent on the domain. Your set of metrics will be different from mine in specifics. A recommendation system can deal with variation in its accuracy results better compared to the fraud detector system. However, the model that consists of four aspects, budgets that consider time intervals, and ownership that is clearly stated has proved to be effective throughout the models involving ML that I have used before. The aim is not about preventing all cases of model deterioration. It is about understanding it, comprehending why it happens, and having the power to correct it before it shatters user trust.",
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<p>ML systems decay gradually instead of breaking suddenly, so we need error budgets for model accuracy, data freshness, and fairness — not just uptime.</p>
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<div class="content-html"><p dir="ltr">Here's a problem I've seen happen far too often: your recommendation system is functioning, spitting out results in milliseconds, and meeting all its infrastructure SLAs. Everything is looking rosy in the dashboard world. Yet engagement has plummeted by 40% because your model has been pointless for several weeks.</p>
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<p dir="ltr">On behalf of your traditional error budget? You're golden. According to your product team? The system is broken.</p>
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||
<p dir="ltr">ML systems fail in ways that were not accounted for in classical <a href="https://dzone.com/articles/the-guide-to-sre-principles">SRE practices</a>. A model does not 'go down'; it gradually deteriorates. Data pipelines can be 'working' while providing garbage to the model. And you won't even realize this until users start to complain or, worst, quietly depart.</p>
|
||
<p dir="ltr">The past few years spent breaking and fixing ML systems have taught me that we need a paradigm shift in our error budget. Here's how it works.</p>
|
||
<h2 dir="ltr">Understanding the Limitations of Conventional Error Budgets</h2>
|
||
<p dir="ltr">The challenge here is that "reliability" in ML does not live on a one-dimensional spectrum. Your <a href="https://dzone.com/articles/everything-you-should-know-about-apis">API</a> could be functioning correctly even if your model is not working. Your model could be working correctly even if your data pipeline is providing stale features to your model. You could be doing great on your aggregate numbers even if you're treating some users unfairly.</p>
|
||
<p dir="ltr">What I've found is that you need to break down four different error budgets.</p>
|
||
<h2 dir="ltr">Mapping These to Actual Error Budgets</h2>
|
||
<p dir="ltr">Before delving into each dimension, I must clarify the application of these to conventional SRE error budgets — not merely health checks:</p>
|
||
<p dir="ltr">For each dimension, you require:</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• <strong>SLI (service level indicator)</strong>: What you're measuring</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• <strong>SLO (service level objective)</strong>: Your target over time</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• <strong>Error budget</strong>: How much you can miss the SLO before you take action</p>
|
||
<p dir="ltr">Here's what model quality means with concrete examples:</p>
|
||
<p dir="ltr" style="margin-left: 20px;"><strong>SLI</strong>: Accuracy of the model compared with the baseline, hourly</p>
|
||
<p dir="ltr" style="margin-left: 20px;"><strong>SLO</strong>: Accuracy ≥ 92% of Baseline over the rolling 7 days</p>
|
||
<p dir="ltr" style="margin-left: 20px;"><strong>Error budget</strong>: 8% allowable error in 7 days</p>
|
||
<p dir="ltr" style="margin-left: 20px;"><strong>Burn rate</strong>: Monitor hourly; warn for burning above 10% of budget daily</p>
|
||
<p dir="ltr">The main difference versus an error budget is that you're measuring degradation relative to a known-good state as opposed to just measuring success or failure. The math is exactly the same in both cases — a time budget that gets spent if you don't meet your SLO.</p>
|
||
<p dir="ltr">Now, let's consider every dimension one by one:</p>
|
||
<h2 dir="ltr">1. Infrastructure Error Budget</h2>
|
||
<p dir="ltr">These are your standard SRE metrics: uptime, latency, and success rate of requests. It's old news, but you should have this as your baseline.</p>
|
||
<p dir="ltr"><strong>What I monitor</strong>: 99.95% availability, latency of sub-150ms at p95, 99.9% success rate</p>
|
||
<h2 dir="ltr">2. Model Quality Error Budget</h2>
|
||
<p dir="ltr">This is where it gets fascinating. You must specify at what point you are willing to let the degradation of your model become noisy.</p>
|
||
<p dir="ltr"><strong>What I track</strong>:</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Model accuracy vs baseline accuracy (typically up to 8% loss)</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Percentage of low-confidence predictions</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Distribution of feature drift via statistical tests</p>
|
||
<p dir="ltr">Here's how I can determine degradation:</p>
|
||
<div class="codeMirror-wrapper newest" contenteditable="false">
|
||
<div contenteditable="false">
|
||
<div class="codeHeader">
|
||
<div class="nameLanguage">
|
||
Python
|
||
</div><i class="icon-cancel-circled-1 cm-remove"> </i>
|
||
</div>
|
||
<div class="codeMirror-code--wrapper" data-code="# Compare Current Performance with Your Personal Benchmark
|
||
accuracy_degradation = (baseline_accuracy - current_accuracy) / baseline_accuracy
|
||
budget_burn_rate = accuracy_degradation / acceptable_degradation" data-lang="text/x-python">
|
||
<pre><code lang="text/x-python"># Compare Current Performance with Your Personal Benchmark
|
||
accuracy_degradation = (baseline_accuracy - current_accuracy) / baseline_accuracy
|
||
budget_burn_rate = accuracy_degradation / acceptable_degradation</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p dir="ltr"><br></p>
|
||
<p dir="ltr"><strong>Real example</strong>: Accuracy decreased from 95% to 93%, my threshold is 8%</p>
|
||
<p dir="ltr">As for drift detection, I employ the Kolmogorov-Smirnov test:</p>
|
||
<div class="codeMirror-wrapper newest" contenteditable="false">
|
||
<div contenteditable="false">
|
||
<div class="codeHeader">
|
||
<div class="nameLanguage">
|
||
Python
|
||
</div><i class="icon-cancel-circled-1 cm-remove"> </i>
|
||
</div>
|
||
<div class="codeMirror-code--wrapper" data-code="# Verify distribution of features has changed
|
||
from scipy.stats import ks_2samp
|
||
|
||
statistic, p_value = ks_2samp(baseline_features, current_features)
|
||
drift_alert = p_value < 0.05" data-lang="text/x-python">
|
||
<pre><code lang="text/x-python"># Verify distribution of features has changed
|
||
from scipy.stats import ks_2samp
|
||
|
||
statistic, p_value = ks_2samp(baseline_features, current_features)
|
||
drift_alert = p_value < 0.05</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p dir="ltr"><br></p>
|
||
<p dir="ltr"><strong>One thing that bit me</strong>: Tie your model accuracy metrics to business metrics. Rather than accuracy percentages, track something your PM cares about — for example, "click-through rate stays within 95% of baseline."</p>
|
||
<h2 dir="ltr">3. Data Quality Error Budget</h2>
|
||
<p dir="ltr">Garbage in, garbage out. However, the ML system "garbage" needs a different definition.</p>
|
||
<p dir="ltr">What matters:</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Feature completeness score (my target is 99%+)</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Feature freshness degree (how many features are stale?)</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Schema violations</p>
|
||
<p dir="ltr"><strong>Simple quality check</strong>:</p>
|
||
<div class="codeMirror-wrapper newest" contenteditable="false">
|
||
<div contenteditable="false">
|
||
<div class="codeHeader">
|
||
<div class="nameLanguage">
|
||
Python
|
||
</div><i class="icon-cancel-circled-1 cm-remove"> </i>
|
||
</div>
|
||
<div class="codeMirror-code--wrapper" data-code="def simple_quality_check(features):
|
||
missing_rate = missing_features / total_features
|
||
stale_rate = stale_features / total_features
|
||
data_quality_score = min(1 - missing_rate, 1 - stale_rate)
|
||
meets_sli = data_quality_score > 0.99
|
||
|
||
" data-lang="text/x-python">
|
||
<pre><code lang="text/x-python">def simple_quality_check(features):
|
||
missing_rate = missing_features / total_features
|
||
stale_rate = stale_features / total_features
|
||
data_quality_score = min(1 - missing_rate, 1 - stale_rate)
|
||
meets_sli = data_quality_score > 0.99
|
||
|
||
</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p dir="ltr"><br></p>
|
||
<p dir="ltr">Traditional data pipelines only cared about having a correct schema. When working with <a href="https://dzone.com/articles/machine-learning-unleashing-the-power-of-artificia">machine learning</a>, you also want to ensure that your data features are fresh enough and that your distributions look fairly regular. I've been burned before working on pipelines that "worked" but passed day-old data, making our model irrelevant.</p>
|
||
<h2 dir="ltr">4. Fairness Error Budget</h2>
|
||
<p dir="ltr">In your case, fairness can be either desirable or mandatory. Regardless, it should be tracked.</p>
|
||
<p dir="ltr">What I monitor:</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Differences in accuracy across demographic groups (this is under 5%)</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• False positive rate parity across segments</p>
|
||
<p dir="ltr">To calculate disparate impact:</p>
|
||
<div class="codeMirror-wrapper newest" contenteditable="false">
|
||
<div contenteditable="false">
|
||
<div class="codeHeader">
|
||
<div class="nameLanguage">
|
||
Python
|
||
</div><i class="icon-cancel-circled-1 cm-remove"> </i>
|
||
</div>
|
||
<div class="codeMirror-code--wrapper" data-code="# Determine disparate impact
|
||
group_A_rate = predictions[group == 'A'].mean()
|
||
group_B_rate = predictions[group == 'B'].mean()
|
||
|
||
disparity = abs(group_A_rate - group_B_rate)
|
||
violation = disparity > 0.05 # flag if over 5%" data-lang="text/x-python">
|
||
<pre><code lang="text/x-python"># Determine disparate impact
|
||
group_A_rate = predictions[group == 'A'].mean()
|
||
group_B_rate = predictions[group == 'B'].mean()
|
||
|
||
disparity = abs(group_A_rate - group_B_rate)
|
||
violation = disparity > 0.05 # flag if over 5%</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p dir="ltr"><br></p>
|
||
<p dir="ltr">There is no such dimension in traditional SRE because a traditional system is not involved in people's decision-making. However, as soon as your machine learning system starts approving loans or ranking candidates for jobs, you want to determine whether your system is treating people fairly.</p>
|
||
<h3 dir="ltr">Critical Caveats</h3>
|
||
<p dir="ltr">Fairness metrics are extremely domain-specific and complex from a legal standpoint. The metrics that I am presenting here are only examples, and demographic parity is not necessarily a good thing for every problem you want to solve. Before using fairness budgets:</p>
|
||
<ul>
|
||
<li>Discuss with lawyers the way in which fairness may be considered in your regulatory environment</li>
|
||
<li>Coordinate with the product and policy teams on identifying the acceptable tradeoffs</li>
|
||
<li>Reflect on whether you have the right to maintain, process, or use sensitive attributes for monitoring purposes</li>
|
||
<li>Do not use simplistic parity checks as the sole indicators of fairness</li>
|
||
</ul>
|
||
<p dir="ltr">In regulated industries such as finance, healthcare, or hiring, you require knowledge that goes beyond the capabilities of any framework.</p>
|
||
<h2 dir="ltr">How to Actually Implement This</h2>
|
||
<h3 dir="ltr">Step 1: Determine How Reliability Applies in Your Business</h3>
|
||
<p dir="ltr">Don't begin with metrics in mind. Begin with conversations instead. "What is a broken model in the eyes of my PM?" "What will make my users grumble?"</p>
|
||
<p dir="ltr">For an ML-driven search functionality, you can choose:</p>
|
||
<ul>
|
||
<li><strong>Infrastructure</strong>: Less than 200 ms (p95)</li>
|
||
<li><strong>Model quality</strong>: Relevance scores greater than 0.85 relative to human assessors</li>
|
||
<li><strong>Data quality</strong>: Less than 1% of queries missing critical features</li>
|
||
<li><strong>Fairness</strong>: Search diversity preserved when considering different user categories</li>
|
||
</ul>
|
||
<h3 dir="ltr">Step 2: Establish Your Baseline</h3>
|
||
<p dir="ltr">Run your system in a stable state for 30 days. Observe what "good" looks like.</p>
|
||
<div class="codeMirror-wrapper newest" contenteditable="false">
|
||
<div contenteditable="false">
|
||
<div class="codeHeader">
|
||
<div class="nameLanguage">
|
||
Python
|
||
</div><i class="icon-cancel-circled-1 cm-remove"> </i>
|
||
</div>
|
||
<div class="codeMirror-code--wrapper" data-code="# Calculate your baseline during a stable period
|
||
baseline = {
|
||
'accuracy': np.percentile(stable_metrics['accuracy'], 50),
|
||
'p95_latency': np.percentile(stable_metrics['latency'], 95),
|
||
'drift_threshold': calculate_drift_threshold(stable_features)
|
||
}" data-lang="text/x-python">
|
||
<pre><code lang="text/x-python"># Calculate your baseline during a stable period
|
||
baseline = {
|
||
'accuracy': np.percentile(stable_metrics['accuracy'], 50),
|
||
'p95_latency': np.percentile(stable_metrics['latency'], 95),
|
||
'drift_threshold': calculate_drift_threshold(stable_features)
|
||
}</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p dir="ltr"><br></p>
|
||
<p dir="ltr">This becomes your north star. All else shall be measured from that.</p>
|
||
<h3 dir="ltr">Step 3: Define Ownership</h3>
|
||
<p dir="ltr">This is crucial. Each dimension must have a "clear owner" to make decisions and take actions:</p>
|
||
<p dir="ltr"><strong>Infrastructure budget → SRE owns</strong>:</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Right to suspend deployments</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Authority to reverse modifications</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Infrastructure scaling authority</p>
|
||
<p dir="ltr"><strong>Model quality budget → ML engineering owns</strong>:</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Authority for triggering retraining</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Authority to roll back to previous model version</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Power to increase monitoring frequency</p>
|
||
<p dir="ltr"><strong>Data quality budget → data engineering owns</strong>:</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Power to halt data pipelines</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Authority to enable fallback data sources</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Right to disregard upstream data</p>
|
||
<p dir="ltr"><strong>Fairness budget → ML + product + legal own together</strong>:</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Needs a multi-stakeholder decision for any actions</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Product evaluates business impact</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• Legal specifies compliance requirements</p>
|
||
<p dir="ltr" style="margin-left: 20px;">• ML applies technical solutions</p>
|
||
<p dir="ltr">If the budget constraints are conflicting, such that model quality is satisfactory, but fairness is violated, then the more constraining budget prevails. If you have depleted your fairness budget, you cannot just rely on your predictions for satisfactory accuracy.</p>
|
||
<h3 dir="ltr">Step 4: Monitor Everything</h3>
|
||
<p dir="ltr">Establish dashboards to measure all four key dimensions. Here's how I calculate the composite health factors:</p>
|
||
<div class="codeMirror-wrapper newest" contenteditable="false">
|
||
<div contenteditable="false">
|
||
<div class="codeHeader">
|
||
<div class="nameLanguage">
|
||
Python
|
||
</div><i class="icon-cancel-circled-1 cm-remove"> </i>
|
||
</div>
|
||
<div class="codeMirror-code--wrapper" data-code="# Current health across dimensions
|
||
dimensions = {
|
||
'infrastructure': 0.95, # meeting 95% of SLO
|
||
'model_quality': 0.88, # at 88% of baseline
|
||
'data_quality': 0.98,
|
||
'fairness': 0.96
|
||
}
|
||
|
||
# Weight them according to what is important to your business
|
||
weights = {
|
||
'infrastructure': 0.3,
|
||
'model_quality': 0.35,
|
||
'data_quality': 0.2,
|
||
'fairness': 0.15
|
||
}
|
||
|
||
composite_score = sum(dimensions[d] * weights[d] for d in dimensions)" data-lang="text/x-python">
|
||
<pre><code lang="text/x-python"># Current health across dimensions
|
||
dimensions = {
|
||
'infrastructure': 0.95, # meeting 95% of SLO
|
||
'model_quality': 0.88, # at 88% of baseline
|
||
'data_quality': 0.98,
|
||
'fairness': 0.96
|
||
}
|
||
|
||
# Weight them according to what is important to your business
|
||
weights = {
|
||
'infrastructure': 0.3,
|
||
'model_quality': 0.35,
|
||
'data_quality': 0.2,
|
||
'fairness': 0.15
|
||
}
|
||
|
||
composite_score = sum(dimensions[d] * weights[d] for d in dimensions)</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p dir="ltr"><br></p>
|
||
<p dir="ltr"><strong>Critical note</strong>: The composite score is solely for executive visibility. Hard enforcement always happens on a per-dimension basis. Having a 90% composite score does not supersede a violation in any dimension. You are in violation if you blow your fairness budget.</p>
|
||
<h3 dir="ltr">Step 5: Know What to Do When Budgets Blow Up</h3>
|
||
<p dir="ltr">This list should be recorded prior to having a situation on your hands:</p>
|
||
<ul>
|
||
<li><strong>Infrastructure budget spent out</strong>: Stop deployments, undo changes made, see if scale is required</li>
|
||
<li><strong>Model quality budget used up</strong>: Kick off the retraining process, think about reverting to the former model version, and look at what changed in your dataset</li>
|
||
<li><strong>Data Quality budget exhausted</strong>: Check your upstream data sources, validate your ETL pipeline, turn on feature fallbacks if you have them</li>
|
||
<li><strong>Fairness budget used up</strong>:<strong> </strong>If it's bad, then simply stop making predictions for those subgroups. Don't release it to society until you figure out where you introduced unfair bias and retrain.</li>
|
||
</ul>
|
||
<h2 dir="ltr">A Real Example: Fraud Detection</h2>
|
||
<p dir="ltr">Let me illustrate what I mean with a system for preventing fraud that I built for a fintech company.</p>
|
||
<p dir="ltr">Our error budgets:</p>
|
||
<ul>
|
||
<li><strong>Infrastructure</strong>: 99.99% uptime, under 100ms at p95</li>
|
||
<li><strong>Model quality</strong>: Precision above 95%, Recall above 90%, False Positive Rate below 2%</li>
|
||
<li><strong>Data quality</strong>: +99.5% feature completion rate, <1% stale features</li>
|
||
<li><strong>Fairness</strong>: FPR differences across merchant types <3%</li>
|
||
</ul>
|
||
<p dir="ltr">Here's what our code for monitoring looked like:</p>
|
||
<div class="codeMirror-wrapper newest" contenteditable="false">
|
||
<div contenteditable="false">
|
||
<div class="codeHeader">
|
||
<div class="nameLanguage">
|
||
Python
|
||
</div><i class="icon-cancel-circled-1 cm-remove"> </i>
|
||
</div>
|
||
<div class="codeMirror-code--wrapper" data-code="# Validating the health of each batch of predictions made
|
||
def check_fraud_detection_health(predictions, features, ground_truth):
|
||
# Did model quality degrade?
|
||
current_precision = precision_score(ground_truth, predictions)
|
||
precision_violation = (baseline - current_precision) / baseline > 0.02
|
||
|
||
# Are features getting stale?
|
||
stale_rate = features[features['age_hours'] > 24].shape[0] / len(features)
|
||
data_violation = stale_rate > 0.01
|
||
|
||
# Fairness issues regarding various merchants?
|
||
fprs = calculate_fpr_by_category(predictions, ground_truth)
|
||
fairness_violation = max(fprs.values()) - min(fprs.values()) > 0.03
|
||
|
||
return any([precision_violation, data_violation, fairness_violation])" data-lang="text/x-python">
|
||
<pre><code lang="text/x-python"># Validating the health of each batch of predictions made
|
||
def check_fraud_detection_health(predictions, features, ground_truth):
|
||
# Did model quality degrade?
|
||
current_precision = precision_score(ground_truth, predictions)
|
||
precision_violation = (baseline - current_precision) / baseline > 0.02
|
||
|
||
# Are features getting stale?
|
||
stale_rate = features[features['age_hours'] > 24].shape[0] / len(features)
|
||
data_violation = stale_rate > 0.01
|
||
|
||
# Fairness issues regarding various merchants?
|
||
fprs = calculate_fpr_by_category(predictions, ground_truth)
|
||
fairness_violation = max(fprs.values()) - min(fprs.values()) > 0.03
|
||
|
||
return any([precision_violation, data_violation, fairness_violation])</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p dir="ltr"><br></p>
|
||
<p dir="ltr"><strong>The "interesting" part</strong>: All these dimensions are actually tested in every prediction batch. It helps you detect issues early, as data quality problems could become evident before affecting model performance.</p>
|
||
<h2 dir="ltr">A Few Things I've Learned</h2>
|
||
<h3 dir="ltr">Use Rolling Windows Where Time-Based Budgets Are Required</h3>
|
||
<p dir="ltr">Monthly budgets aren't really effective in ML either. You may have a bad week when you're retraining your model, but you can't waste the rest of the budget.</p>
|
||
<p dir="ltr"><em>I use 7-day rolling windows instead — still time budgets, but with a sliding window.</em></p>
|
||
<div class="codeMirror-wrapper newest" contenteditable="false">
|
||
<div contenteditable="false">
|
||
<div class="codeHeader">
|
||
<div class="nameLanguage">
|
||
Python
|
||
</div><i class="icon-cancel-circled-1 cm-remove"> </i>
|
||
</div>
|
||
<div class="codeMirror-code--wrapper" data-code="from collections import deque
|
||
|
||
# Measurements deque with maxlen of 7 days * 24 hours
|
||
measurements = deque(maxlen=168)
|
||
measurements.append({'timestamp': now, 'accuracy': current_accuracy})
|
||
|
||
avg_accuracy = sum([m['accuracy'] for m in measurements]) / len(measurements)
|
||
budget_ok = avg_accuracy >= target_accuracy" data-lang="text/x-python">
|
||
<pre><code lang="text/x-python">from collections import deque
|
||
|
||
# Measurements deque with maxlen of 7 days * 24 hours
|
||
measurements = deque(maxlen=168)
|
||
measurements.append({'timestamp': now, 'accuracy': current_accuracy})
|
||
|
||
avg_accuracy = sum([m['accuracy'] for m in measurements]) / len(measurements)
|
||
budget_ok = avg_accuracy >= target_accuracy</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p dir="ltr"><br></p>
|
||
<p dir="ltr">This provides some buffer for recovering from transient problems without having to call bankruptcy for the month. You're still measuring reliability over time (the point of error budgets), but the window slides smoothly rather than restarting each month.</p>
|
||
<h3 dir="ltr">Budget According to What Is Happening</h3>
|
||
<p dir="ltr">In a large product rollout, I'll cut model quality budgets (can't have the model shaming us during peak traffic) while relaxing latency requirements slightly. It's fine to adjust these based on context, just be sure to record the reasoning behind adjustments as they happen.</p>
|
||
<h3 dir="ltr">Be Alert for Cascading Failures</h3>
|
||
<p dir="ltr">"Garbage in, garbage out" applies here, too: bad data input leads to bad model output, which, in turn, results in more attempts and fallbacks, thus more load on the infrastructure. It is where having budgets per dimension comes in handy, as it allows you to zero in on where the problem actually occurred.</p>
|
||
<h2 dir="ltr">Wrapping Up</h2>
|
||
<p dir="ltr">Conventional error budgets account for failures in infrastructure, such as servers becoming unavailable and requests timing out. They fail to account, however, for failure in ML, which occurs in terms of model drift, pipelines with stale features, and biased predictions in terms of user segments.</p>
|
||
<p dir="ltr">This framework identifies these failures early. By monitoring the degradation of model quality with time, you address the issue before it affects users. By monitoring the freshness of the data, you identify the pipeline failures before their impact affects your predictions. By monitoring fairness, you identify bias before it turns into a compliance issue.</p>
|
||
<p dir="ltr">The actual gains in reliability come from the following three sources:</p>
|
||
<ul>
|
||
<li><strong>Earlier detection</strong>: You detect degradation trends before outages</li>
|
||
<li><strong>Root cause clarity</strong>: When quality goes down, you know if it's the infrastructure or the quality of the data</li>
|
||
<li><strong>Clear accountability</strong>: Every factor has a clear owner who has clear action power</li>
|
||
</ul>
|
||
<p dir="ltr">You want to start with the budget on infrastructure and the quality of models. Get familiar with tracking the baseline and calculating the burn rate. Once you're comfortable with that, you can integrate the data quality tracking. Fairness tracking is what you want to do last. It's the most complex aspect of fairness, and it's the most dependent on the domain.</p>
|
||
<p dir="ltr">Your set of metrics will be different from mine in specifics. A recommendation system can deal with variation in its accuracy results better compared to the fraud detector system. However, the model that consists of four aspects, budgets that consider time intervals, and ownership that is clearly stated has proved to be effective throughout the models involving ML that I have used before.</p>
|
||
<p dir="ltr">The aim is not about preventing all cases of model deterioration. It is about understanding it, comprehending why it happens, and having the power to correct it before it shatters user trust.</p></div>
|
||
</div>
|
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|
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|
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|
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<div class="attribution">
|
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<p>Opinions expressed by DZone contributors are their own.</p>
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<a href="/articles/scaling-sre-teams" class="goto-link related-link">Scaling SRE Teams: The Challenges and How To Build a Successful Scaling Framework</a>
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</li>
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<a href="/articles/angular-agentic-ui" class="goto-link related-link">Angular Apps Don’t Need Another Chatbot: Building Agentic UI Workflows With TypeScript</a>
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</li>
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<a href="/articles/ai-agent-frameworks" class="goto-link related-link">A Field Guide to AI Agent Frameworks</a>
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<a href="/articles/building-metadata-driven-quality-framework" class="goto-link related-link">Stop Hardcoding Database Checks: Building a Metadata-Driven Data Quality Framework</a>
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x-on:click="option.onClick"
|
||
x-bind:disabled="option.isDisabled"
|
||
></button>
|
||
</template>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<script>
|
||
document.addEventListener("alpine:init", () => {
|
||
Alpine.store("modal_engagement_error", {
|
||
_open: false,
|
||
title: "Oops! Something Went Wrong",
|
||
bodyText: null,
|
||
options: [{"action":"dismiss","text":"Close"}],
|
||
closeable: true,
|
||
dismissible: true,
|
||
open(data) {
|
||
if (data) {
|
||
if (data.title) {
|
||
this.title = data.title;
|
||
}
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||
if (data.bodyText) {
|
||
this.bodyText = data.bodyText;
|
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}
|
||
if (data.options) {
|
||
this.options = data.options;
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}
|
||
if (data.dismissAfterMs) {
|
||
setTimeout(() => this._open = false, data.dismissAfterMs);
|
||
}
|
||
}
|
||
this.updateOptions();
|
||
this._open = true;
|
||
},
|
||
close() {
|
||
// Manually click the buttons in which should run on dismiss.
|
||
// This is to keep the context. Calling this[option.action]() will not work.
|
||
for (let option of this.options) {
|
||
if (option.runOnDismiss) {
|
||
const element = document.querySelector('#button_modal_engagement_error_' + option.index);
|
||
element.click();
|
||
}
|
||
}
|
||
this._open = false;
|
||
},
|
||
updateOptions() {
|
||
const btnStyle = this.options.length === 2 ? ' btn-default' : '';
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||
this.options = this.options.map((o, idx) => ({
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||
...o,
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||
index: idx,
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||
classList: o.classList ? o.classList : (idx === 0 ? "btn btn-primary hov-brighten" : "btn hov-underline" + btnStyle),
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||
onClick: function() {
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||
if (o.action !== "dismiss") {
|
||
this[o.action]();
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||
}
|
||
this.$store["modal_engagement_error"].close();
|
||
},
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isDisabled: function() {
|
||
return Object.hasOwn(o, 'disabled') && this[o.disabled]();
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},
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canDisplay: function() {
|
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return !Object.hasOwn(o, 'condition') || this[o.condition]();
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}
|
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}));
|
||
},
|
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init() {
|
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this.updateOptions();
|
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}
|
||
});
|
||
});
|
||
</script>
|
||
|
||
<link rel="stylesheet" media="all" href="https://dz2cdn1.dzone.com/themes/dz20/ftl/footer/styles.css">
|
||
|
||
<div id="ftl-footer">
|
||
<div class="container-fluid footerOuter" style="padding-bottom: 90px;">
|
||
<div class="row">
|
||
<div class="col-md-12">
|
||
<div class="container">
|
||
<div class="row footer">
|
||
<div class="col-md-12 footerWidget">
|
||
<div class="row footerContainer footer">
|
||
<div class="left col-xs-12 col-sm-7">
|
||
<div class="col-xs-12 social-media-icons footer-mobile">
|
||
<ul class="icons-only">
|
||
<li class="rss-icon" id="rss-footer-1">
|
||
<a href="/pages/feeds" target="_blank" rel="noreferrer noopener">
|
||
<svg role="img" viewBox="0 0 24 24" class="w-4 h-4 mt-1.75 fill-current" aria-label="Follow our RSS feeds">
|
||
<title>RSS</title>
|
||
<path d="M19.199 24C19.199 13.467 10.533 4.8 0 4.8V0c13.165 0 24 10.835 24 24h-4.801zM3.291 17.415c1.814 0 3.293 1.479 3.293 3.295 0 1.813-1.485 3.29-3.301 3.29C1.47 24 0 22.526 0 20.71s1.475-3.294 3.291-3.295zM15.909 24h-4.665c0-6.169-5.075-11.245-11.244-11.245V8.09c8.727 0 15.909 7.184 15.909 15.91z"></path>
|
||
</svg>
|
||
</a>
|
||
</li>
|
||
<li class="twitter-icon">
|
||
<a href="https://twitter.com/DZoneInc" target="_blank" rel="noreferrer noopener">
|
||
<svg role="img" viewBox="0 0 24 24" aria-label="Follow us on X">
|
||
<title>X</title>
|
||
<path d="M14.234 10.162 22.977 0h-2.072l-7.591 8.824L7.251 0H.258l9.168 13.343L.258 24H2.33l8.016-9.318L16.749 24h6.993zm-2.837 3.299-.929-1.329L3.076 1.56h3.182l5.965 8.532.929 1.329 7.754 11.09h-3.182z"></path>
|
||
</svg>
|
||
</a>
|
||
</li>
|
||
<li class="facebook-icon">
|
||
<a href="https://www.facebook.com/DZoneInc" target="_blank" rel="noreferrer noopener">
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||
<svg role="img" viewBox="0 0 24 24" aria-label="Follow us on Facebook">
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||
<title>Facebook</title>
|
||
<path d="M9.101 23.691v-7.98H6.627v-3.667h2.474v-1.58c0-4.085 1.848-5.978 5.858-5.978.401 0 .955.042 1.468.103a8.68 8.68 0 0 1 1.141.195v3.325a8.623 8.623 0 0 0-.653-.036 26.805 26.805 0 0 0-.733-.009c-.707 0-1.259.096-1.675.309a1.686 1.686 0 0 0-.679.622c-.258.42-.374.995-.374 1.752v1.297h3.919l-.386 2.103-.287 1.564h-3.246v8.245C19.396 23.238 24 18.179 24 12.044c0-6.627-5.373-12-12-12s-12 5.373-12 12c0 5.628 3.874 10.35 9.101 11.647Z"></path>
|
||
</svg>
|
||
</a>
|
||
</li>
|
||
<li class="linkedin-icon">
|
||
<a href="https://www.linkedin.com/company/dzone/" target="_blank"
|
||
rel="noreferrer noopener">
|
||
<svg viewBox="0 0 24 25" fill="none" aria-label="Follow us on LinkedIn">
|
||
<path d="M6.20062 21.2143H1.84688V7.194H6.20062V21.2143ZM4.02141 5.2815C2.62922 5.2815 1.5 4.12838 1.5 2.73619C1.5 2.06747 1.76565 1.42614 2.2385 0.953285C2.71136 0.48043 3.35269 0.214783 4.02141 0.214783C4.69012 0.214783 5.33145 0.48043 5.80431 0.953285C6.27716 1.42614 6.54281 2.06747 6.54281 2.73619C6.54281 4.12838 5.413 5.2815 4.02141 5.2815ZM22.4953 21.2143H18.1509V14.3893C18.1509 12.7628 18.1181 10.6768 15.8873 10.6768C13.6237 10.6768 13.2769 12.444 13.2769 14.2721V21.2143H8.92781V7.194H13.1034V9.1065H13.1644C13.7456 8.00494 15.1655 6.84244 17.2838 6.84244C21.69 6.84244 22.5 9.744 22.5 13.5128V21.2143H22.4953Z" fill="currentColor"></path>
|
||
</svg>
|
||
</a>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
|
||
<div class="top-section col-xs-12">
|
||
<div class="col-xs-12 col-sm-6">
|
||
<p class="section-header">ABOUT US</p>
|
||
<ul class="link-group">
|
||
<li><a href="/pages/about" rel="noreferrer noopener">About DZone</a></li>
|
||
<li><a href="/cdn-cgi/l/email-protection#45363035352a373105213f2a2b206b262a28" rel="noreferrer noopener">Support and feedback</a></li>
|
||
<li><a href="/pages/dzone-community-research">Community research</a></li>
|
||
</ul>
|
||
</div>
|
||
<div class="col-xs-12 col-sm-6">
|
||
<p class="section-header">ADVERTISE</p>
|
||
<ul class="link-group">
|
||
<li><a href="https://advertise.dzone.com" target="_blank" rel="noreferrer noopener">Advertise with DZone</a></li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="bottom-section col-xs-12">
|
||
<div class="col-xs-12 col-sm-6">
|
||
<p class="section-header">CONTRIBUTE ON DZONE</p>
|
||
<ul class="bottom-top-list link-group">
|
||
<li><a href="/articles/dzones-article-submission-guidelines">Article Submission Guidelines</a></li>
|
||
<li><a href="/pages/contribute" rel="noreferrer noopener">Become a Contributor</a></li>
|
||
<li><a href="/pages/core" rel="noreferrer noopener">Core Program</a></li>
|
||
<li><a href="/writers-zone" rel="noreferrer noopener">Visit the Writers' Zone</a></li>
|
||
</ul>
|
||
|
||
<p class="section-header">LEGAL</p>
|
||
<ul class="link-group">
|
||
<li><a href="https://technologyadvice.com/terms-conditions/" target="_blank" rel="noreferrer noopener">Terms of Service</a></li>
|
||
<li><a href="https://technologyadvice.com/privacy-policy/" target="_blank" rel="noreferrer noopener">Privacy Policy</a></li>
|
||
</ul>
|
||
</div>
|
||
<div class="col-xs-12 col-sm-6">
|
||
<p class="section-header">CONTACT US</p>
|
||
<ul class="link-group">
|
||
<li>3343 Perimeter Hill Drive</li>
|
||
<li>Suite 215</li>
|
||
<li>Nashville, TN 37211</li>
|
||
<li><a href="/cdn-cgi/l/email-protection#55262025253a272115312f3a3b307b363a38" rel="noreferrer noopener"><span class="__cf_email__" data-cfemail="e794929797889593a7839d888982c984888a">[email protected]</span></a></li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="right col-xs-12 col-sm-5">
|
||
|
||
<p class="connect-text">Let's be friends:</p>
|
||
<div class="col-xs-12 social-media-icons footer-wide">
|
||
<ul class="icons-only">
|
||
<li class="rss-icon" id="rss-footer-1">
|
||
<a href="/pages/feeds" target="_blank" rel="noreferrer noopener">
|
||
<svg role="img" viewBox="0 0 24 24" aria-label="Follow our RSS feeds">
|
||
<title>RSS</title>
|
||
<path d="M19.199 24C19.199 13.467 10.533 4.8 0 4.8V0c13.165 0 24 10.835 24 24h-4.801zM3.291 17.415c1.814 0 3.293 1.479 3.293 3.295 0 1.813-1.485 3.29-3.301 3.29C1.47 24 0 22.526 0 20.71s1.475-3.294 3.291-3.295zM15.909 24h-4.665c0-6.169-5.075-11.245-11.244-11.245V8.09c8.727 0 15.909 7.184 15.909 15.91z"></path>
|
||
</svg>
|
||
</a>
|
||
</li>
|
||
<li class="twitter-icon">
|
||
<a href="https://twitter.com/DZoneInc" target="_blank" rel="noreferrer noopener">
|
||
<svg role="img" viewBox="0 0 24 24" aria-label="Follow us on X">
|
||
<title>X</title>
|
||
<path d="M14.234 10.162 22.977 0h-2.072l-7.591 8.824L7.251 0H.258l9.168 13.343L.258 24H2.33l8.016-9.318L16.749 24h6.993zm-2.837 3.299-.929-1.329L3.076 1.56h3.182l5.965 8.532.929 1.329 7.754 11.09h-3.182z"></path>
|
||
</svg>
|
||
</a>
|
||
</li>
|
||
<li class="facebook-icon">
|
||
<a href="https://www.facebook.com/DZoneInc" target="_blank" rel="noreferrer noopener">
|
||
<svg role="img" viewBox="0 0 24 24" aria-label="Follow us on Facebook">
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||
<title>Facebook</title>
|
||
<path d="M9.101 23.691v-7.98H6.627v-3.667h2.474v-1.58c0-4.085 1.848-5.978 5.858-5.978.401 0 .955.042 1.468.103a8.68 8.68 0 0 1 1.141.195v3.325a8.623 8.623 0 0 0-.653-.036 26.805 26.805 0 0 0-.733-.009c-.707 0-1.259.096-1.675.309a1.686 1.686 0 0 0-.679.622c-.258.42-.374.995-.374 1.752v1.297h3.919l-.386 2.103-.287 1.564h-3.246v8.245C19.396 23.238 24 18.179 24 12.044c0-6.627-5.373-12-12-12s-12 5.373-12 12c0 5.628 3.874 10.35 9.101 11.647Z"></path>
|
||
</svg>
|
||
</a>
|
||
</li>
|
||
<li class="linkedin-icon">
|
||
<a href="https://www.linkedin.com/company/dzone/" target="_blank"
|
||
rel="noreferrer noopener">
|
||
<svg viewBox="0 0 24 25" fill="none" aria-label="Follow us on LinkedIn">
|
||
<path d="M6.20062 21.2143H1.84688V7.194H6.20062V21.2143ZM4.02141 5.2815C2.62922 5.2815 1.5 4.12838 1.5 2.73619C1.5 2.06747 1.76565 1.42614 2.2385 0.953285C2.71136 0.48043 3.35269 0.214783 4.02141 0.214783C4.69012 0.214783 5.33145 0.48043 5.80431 0.953285C6.27716 1.42614 6.54281 2.06747 6.54281 2.73619C6.54281 4.12838 5.413 5.2815 4.02141 5.2815ZM22.4953 21.2143H18.1509V14.3893C18.1509 12.7628 18.1181 10.6768 15.8873 10.6768C13.6237 10.6768 13.2769 12.444 13.2769 14.2721V21.2143H8.92781V7.194H13.1034V9.1065H13.1644C13.7456 8.00494 15.1655 6.84244 17.2838 6.84244C21.69 6.84244 22.5 9.744 22.5 13.5128V21.2143H22.4953Z" fill="currentColor"></path>
|
||
</svg>
|
||
</a>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<script data-cfasync="false" src="/cdn-cgi/scripts/5c5dd728/cloudflare-static/email-decode.min.js"></script><script>
|
||
const articleId = 3629364;
|
||
const likes = 1;
|
||
const assetDomain = 'https://dz2cdn1.dzone.com';
|
||
const codemirrorVars = {
|
||
modeURI: 'https://dz2cdn1.dzone.com/themes/dz20/lib/codemirror/mode/',
|
||
requiredScripts: [
|
||
'https://dz2cdn1.dzone.com/themes/dz20/lib/codemirror/lib/codemirror.js',
|
||
'https://dz2cdn1.dzone.com/themes/dz20/lib/codemirror/addon/mode/overlay.js',
|
||
'https://dz2cdn1.dzone.com/themes/dz20/lib/codemirror/addon/mode/multiplex.js',
|
||
'https://dz2cdn1.dzone.com/themes/dz20/lib/codemirror/mode/meta.js'
|
||
]
|
||
};
|
||
|
||
const gptTags = {
|
||
'zone': 'Frameworks',
|
||
'topicTag': 'Site reliability engineering,framework',
|
||
'company': '',
|
||
'siteSection': 'Zones',
|
||
'articleCategory': 'analysis',
|
||
'nodeID': '3629364',
|
||
'authorID': '5393984',
|
||
'publishYear': '2026',
|
||
'publishMonth': '02',
|
||
'jobRole': '',
|
||
'companySize': '',
|
||
'env': 'prod'
|
||
};
|
||
|
||
const minCommentChar = 10;
|
||
</script>
|
||
<script async>const width = window.innerWidth;
|
||
|
||
const metadata = {
|
||
'top': {
|
||
'position': 'top',
|
||
'slot': 'dz2_article_billboard_new',
|
||
},
|
||
'sponsorLogo': {
|
||
'position': 'zoneHomepage',
|
||
'slot': 'dz2_homepage_sponsor_logo',
|
||
'refreshable': false
|
||
},
|
||
'sidebar1': {
|
||
'position': 'sidebar',
|
||
'slot': 'dz2_article_halfpage_new',
|
||
'minWidthToShow': 1024
|
||
},
|
||
'topBumper': {
|
||
'position': 'top',
|
||
'slot': 'dz2_bumper_text_ad',
|
||
'minWidthToShow': 1024
|
||
},
|
||
'bottomBumper': {
|
||
'position': 'bottom',
|
||
'slot': 'dz2_bumper_text_ad',
|
||
'minWidthToShow': 1024
|
||
},
|
||
'bottom': {
|
||
'position': 'bottom',
|
||
'slot': 'dz2_article_bottom',
|
||
},
|
||
'bottomStickyFooter': {
|
||
'position': 'sticky',
|
||
'slot': 'dz2_sticky_footer_leaderboard',
|
||
},
|
||
'partner': {
|
||
'slot': 'dz2_partner_resource_link',
|
||
},
|
||
'branded': {
|
||
'slot': 'dz2_branded_content',
|
||
'refreshable': false
|
||
},
|
||
'topicBillboard': {
|
||
'position': ['top', 'zoneHomepage'],
|
||
'slot': 'dz2_topic_billboard',
|
||
},
|
||
'listPageSidebar': {
|
||
'position': 'sidebar',
|
||
'slot': 'dz2_list_page_sidebar',
|
||
'minWidthToShow': 1024
|
||
},
|
||
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|
||
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|
||
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|
||
},
|
||
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|
||
'position': ['bottom', 'zoneList'],
|
||
'slot': 'dz2_list_page_leaderboard_2',
|
||
},
|
||
'homepageLeaderboard': {
|
||
'position': 'top',
|
||
'slot': 'dz2_homepage_leaderboard',
|
||
},
|
||
'homepageLeaderboard2': {
|
||
'position': 'bottom',
|
||
'slot': 'dz2_homepage_leaderboard_2',
|
||
},
|
||
'skybox': {
|
||
'position': 'above nav',
|
||
'slot': 'dz_skybox',
|
||
'refreshable': false
|
||
},
|
||
'inline': {
|
||
'position': 'inline',
|
||
'slot': 'dz2_inline-article-display',
|
||
}
|
||
};
|
||
|
||
var campaign = new URLSearchParams(window.location.search).get("adTargeting_campaign");
|
||
|
||
window.googletag = window.googletag || { cmd: [] };
|
||
|
||
if (campaign) {
|
||
window.googletag.cmd.push(function() {
|
||
window.googletag.setConfig({ targeting: { campaign }});
|
||
});
|
||
}
|
||
|
||
let lastHeader = null;
|
||
|
||
const topContainer = document.querySelector('#top-bumper-container');
|
||
const bottomContainer = document.querySelector('#bottom-bumper-container');
|
||
|
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function GAM_getPersistentValue(key) {
|
||
const stored = JSON.parse(localStorage.getItem(key));
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|
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if (!stored) {
|
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return null;
|
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}
|
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|
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const { value, expiration } = stored;
|
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|
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if (expiration && Date.now() >= expiration) {
|
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localStorage.removeItem(key);
|
||
return null;
|
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}
|
||
|
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return value;
|
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}
|
||
|
||
function GAM_setPersistentValue(key, value, expiration) {
|
||
// 5 minutes from now
|
||
if (!expiration) {
|
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expiration = Date.now() + 300000;
|
||
}
|
||
|
||
const storedValue = JSON.stringify({
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value: value,
|
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expiration: expiration
|
||
});
|
||
|
||
localStorage.setItem(key, storedValue);
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||
|
||
return value;
|
||
}
|
||
|
||
function GAM_synchronousRequest(params) {
|
||
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|
||
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|
||
xhr.setRequestHeader("Content-Type", "application/json");
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||
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if (typeof params.auth_header !== "undefined") {
|
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xhr.setRequestHeader("Authorization", params.auth_header);
|
||
}
|
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|
||
xhr.send(null);
|
||
if (xhr.status === 200) {
|
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return xhr.responseText;
|
||
} else {
|
||
throw new Error('Request failed: ' + xhr.statusText);
|
||
}
|
||
}
|
||
|
||
function GAM_fetch_data(params) {
|
||
let stored_data = GAM_getPersistentValue(params.storage_key);
|
||
|
||
if (stored_data === null) {
|
||
try {
|
||
const response = GAM_synchronousRequest(params);
|
||
const data = JSON.parse(response);
|
||
|
||
// Store and expire after 30 minutes
|
||
return GAM_setPersistentValue(params.storage_key, data, Date.now() + 1800000);
|
||
} catch (error) {
|
||
console.error('Could not get ' + params.storage_key + ' data: ' + error);
|
||
return null;
|
||
}
|
||
} else {
|
||
return stored_data;
|
||
}
|
||
}
|
||
|
||
function GAM_setUpSixSenseTargeting() {
|
||
let meData = GAM_fetch_data({
|
||
url: "https://link.technologyadvice.com/_me",
|
||
storage_key: "ta_me_data"
|
||
});
|
||
let sixSenseData = GAM_fetch_data({
|
||
url: "https://epsilon.6sense.com/v3/company/details",
|
||
auth_header: "Token d20a1b0e892442270cbc4cb6801c0160d28af04c",
|
||
storage_key: "ta_6s_data"
|
||
});
|
||
|
||
function meDataIncludes(i) {
|
||
return meData.tags.includes(i);
|
||
}
|
||
|
||
if (typeof meData !== "undefined" && meData !== null) {
|
||
window.googletag.pubads().setTargeting("visitor_id", meData.vid);
|
||
window.googletag.pubads().setTargeting("user_agent", meData.user_agent);
|
||
|
||
var tags_mapping = {
|
||
"is_datacenter": ["site.is-datacenter"],
|
||
"is_suspected_bot": ["site.suspected-bad-bot", "site.bad-bot"],
|
||
"is_ta_user": ["site.is-ta-user"],
|
||
"is_crawler": ["site.user-agent-blocked"],
|
||
"is_ad_blocked": ["site.is-ad-blocked"],
|
||
};
|
||
|
||
for (var key in tags_mapping) {
|
||
if (tags_mapping[key].some(meDataIncludes)) {
|
||
window.googletag.pubads().setTargeting(key, 'true');
|
||
}
|
||
}
|
||
}
|
||
|
||
if (typeof sixSenseData !== "undefined" && sixSenseData !== null) {
|
||
var segment_ids = [];
|
||
if (sixSenseData.segments && sixSenseData.segments.ids && sixSenseData.segments.ids.length) {
|
||
sixSenseData.segments.ids.forEach(function (v) {
|
||
segment_ids.push(v.toString());
|
||
});
|
||
}
|
||
if (typeof segment_ids !== "undefined" && segment_ids.length > 0) {
|
||
window.googletag.pubads().setTargeting("segment_ids_6si", segment_ids);
|
||
}
|
||
}
|
||
}
|
||
|
||
if (width >= 1024 && (topContainer || bottomContainer)) {
|
||
// Dynamically create bumper ad slots for non-mobile devices.
|
||
// Prevents ugly dividers being rendered on empty ad slots (mobile)
|
||
const topBumper = document.createElement('div');
|
||
topBumper.id = 'div-gpt-ad-1435246566686-3';
|
||
topBumper.classList.add('article-bumper', 'article-bumper-top', 'dz2_bumper_text_ad');
|
||
topBumper.setAttribute('data-gpt-slot', 'topBumper');
|
||
|
||
topContainer.appendChild(topBumper);
|
||
|
||
const bottomBumper = document.createElement('div');
|
||
bottomBumper.id = 'div-gpt-ad-1435246566686-4';
|
||
bottomBumper.classList.add('article-bumper', 'article-bumper-bottom', 'dz2_bumper_text_ad');
|
||
bottomBumper.setAttribute('data-gpt-slot', 'bottomBumper');
|
||
|
||
bottomContainer.appendChild(bottomBumper);
|
||
}
|
||
|
||
if (gptTags.zone) {
|
||
gptTags.zone = gptTags.zone.replaceAll(/[\s/]/g, '_')
|
||
.replaceAll(/[^a-zA-Z0-9_]/g, '')
|
||
.toLowerCase();
|
||
}
|
||
|
||
makeAds();
|
||
|
||
function handleSkybox() {
|
||
const skybox = document.querySelector('div.skybox');
|
||
if (skybox) {
|
||
document.body.classList.add('skybox-auto-collapse');
|
||
// observe classlist changes for offsetting sticky ads
|
||
const observer = new MutationObserver((mutations) => {
|
||
mutations.forEach((mutation) => {
|
||
if (mutation.type === 'attributes' && mutation.attributeName === 'class') {
|
||
let offset = 0;
|
||
if (!document.body.classList.contains('skybox-closed') && (
|
||
document.body.classList.contains('ccad-skybox-manualcollapse')
|
||
|| document.body.classList.contains('ccad-skybox-manualexpand')
|
||
)) {
|
||
offset = skybox.getBoundingClientRect().height;
|
||
}
|
||
let eligibleAds = [
|
||
{ element: document.querySelector('.content-right-images'), offset: 108 },
|
||
{ element: document.querySelector('.trending-sidebar'), offset: 88 },
|
||
{ element: document.querySelector('.trending'), offset: 88 }
|
||
];
|
||
for (let ad of eligibleAds) {
|
||
if (ad.element) {
|
||
ad.element.style.top = (ad.offset + offset) + 'px';
|
||
}
|
||
}
|
||
}
|
||
});
|
||
});
|
||
observer.observe(document.body, { attributes: true });
|
||
}
|
||
}
|
||
|
||
function isHeaderEligible(header) {
|
||
const range = document.createRange();
|
||
range.setStartAfter(lastHeader);
|
||
range.setEndBefore(header);
|
||
|
||
return range.toString().trim().length >= 600;
|
||
}
|
||
|
||
function placeInlineAds() {
|
||
const excludedContent = document.querySelectorAll('#ftl-article.branded-content, #ftl-article.sponsored');
|
||
|
||
if (excludedContent.length > 0) {
|
||
return;
|
||
}
|
||
|
||
const headers = Array.from(document.querySelectorAll('.content-html h2')).splice(1);
|
||
|
||
let globalIndex = 12;
|
||
|
||
for (let i = 0; i < headers.length; i++) {
|
||
if (!lastHeader || isHeaderEligible(headers[i])) {
|
||
const element = document.createElement('div');
|
||
element.id = 'div-gpt-ad-1435246566686-' + globalIndex;
|
||
element.classList.add('inline-display', 'dz2_inline-article-display');
|
||
element.setAttribute('data-gpt-slot', 'inline');
|
||
headers[i].before(element);
|
||
lastHeader = element;
|
||
globalIndex++;
|
||
}
|
||
}
|
||
}
|
||
|
||
function makeAds() {
|
||
handleSkybox();
|
||
placeInlineAds();
|
||
|
||
const script = document.createElement('script');
|
||
script.src = 'https://i6ByW9Zmz4ncxhHkb.ay.delivery/manager/i6ByW9Zmz4ncxhHkb';
|
||
script.type = 'text/javascript';
|
||
script.referrerPolicy = 'no-referrer-when-downgrade';
|
||
|
||
document.head.appendChild(script);
|
||
|
||
window.googletag.cmd.push(function() {
|
||
const containers = document.querySelectorAll('div[data-gpt-slot]');
|
||
|
||
for (let container of containers) {
|
||
const div = container.getAttribute('data-gpt-slot');
|
||
const meta = metadata[div];
|
||
|
||
if (meta.minWidthToShow && width < meta.minWidthToShow) {
|
||
continue;
|
||
}
|
||
|
||
window.googletag.pubads().setTargeting('hostname', window.location.hostname);
|
||
|
||
Object.keys(gptTags).forEach(function(key) {
|
||
window.googletag.pubads().setTargeting(key, gptTags[key]);
|
||
});
|
||
|
||
window.googletag.pubads().addEventListener('slotRenderEnded', (event) => {
|
||
window.requestAnimationFrame(() => {
|
||
var slotId = event.slot.getSlotElementId();
|
||
|
||
if (!slotId.includes('__ayManagerEnv__')) {
|
||
return;
|
||
}
|
||
|
||
var className = slotId.split('__ayManagerEnv__')[0];
|
||
var slotName = className.replace('dz2_', '').replace('dz_', '').replace(/_\d+$/, '');
|
||
var unitName = meta.slot.replace('dz2_', '').replace('dz_', '').replace(/_\d+$/, '');
|
||
|
||
if (slotName !== unitName) {
|
||
return;
|
||
}
|
||
|
||
var elem = document.getElementById(slotId);
|
||
|
||
if (!elem) {
|
||
console.warn('Ad element missing', slotId);
|
||
return;
|
||
}
|
||
|
||
// Ad unit did not fill, collapse slot
|
||
if (event.isEmpty && elem.parentElement) {
|
||
elem.parentElement.style.display = 'none';
|
||
}
|
||
});
|
||
});
|
||
}
|
||
|
||
GAM_setUpSixSenseTargeting();
|
||
});
|
||
}
|
||
</script>
|
||
<script async>// InMobi Choice. Consent Manager Tag v3.0 (for TCF 2.2)
|
||
!function(){var e=window.location.hostname,t=document.createElement("script"),n=document.getElementsByTagName("script")[0],a="https://cmp.inmobi.com".concat("/choice/","vPn77x7pBG57Y","/","dzone.com","/choice.js?tag_version=V3"),p=0;t.async=!0,t.type="text/javascript",t.src=a,n.parentNode.insertBefore(t,n),function(){for(var e,t="__tcfapiLocator",n=[],a=window;a;){try{if(a.frames[t]){e=a;break}}catch(e){}if(a===window.top)break;a=a.parent}e||(!function e(){var n=a.document,p=!!a.frames[t];if(!p)if(n.body){var s=n.createElement("iframe");s.style.cssText="display:none",s.name=t,n.body.appendChild(s)}else setTimeout(e,5);return!p}(),a.__tcfapi=function(){var e,t=arguments;if(!t.length)return n;if("setGdprApplies"===t[0])t.length>3&&2===t[2]&&"boolean"==typeof t[3]&&(e=t[3],"function"==typeof t[2]&&t[2]("set",!0));else if("ping"===t[0]){var a={gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"};"function"==typeof t[2]&&t[2](a)}else"init"===t[0]&&"object"==typeof t[3]&&(t[3]=Object.assign(t[3],{tag_version:"V3"})),n.push(t)},a.addEventListener("message",(function(e){var t="string"==typeof e.data,n={};try{n=t?JSON.parse(e.data):e.data}catch(e){}var a=n.__tcfapiCall;a&&window.__tcfapi(a.command,a.version,(function(n,p){var s={__tcfapiReturn:{returnValue:n,success:p,callId:a.callId}};t&&(s=JSON.stringify(s)),e&&e.source&&e.source.postMessage&&e.source.postMessage(s,"*")}),a.parameter)}),!1))}(),function(){const e=["2:tcfeuv2","6:uspv1","7:usnatv1","8:usca","9:usvav1","10:uscov1","11:usutv1","12:usctv1"];window.__gpp_addFrame=function(e){if(!window.frames[e])if(document.body){var t=document.createElement("iframe");t.style.cssText="display:none",t.name=e,document.body.appendChild(t)}else window.setTimeout(window.__gpp_addFrame,10,e)},window.__gpp_stub=function(){var t=arguments;if(__gpp.queue=__gpp.queue||[],__gpp.events=__gpp.events||[],!t.length||1==t.length&&"queue"==t[0])return __gpp.queue;if(1==t.length&&"events"==t[0])return __gpp.events;var n=t[0],a=t.length>1?t[1]:null,p=t.length>2?t[2]:null;if("ping"===n)a({gppVersion:"1.1",cmpStatus:"stub",cmpDisplayStatus:"hidden",signalStatus:"not ready",supportedAPIs:e,cmpId:10,sectionList:[],applicableSections:[-1],gppString:"",parsedSections:{}},!0);else if("addEventListener"===n){"lastId"in __gpp||(__gpp.lastId=0),__gpp.lastId++;var s=__gpp.lastId;__gpp.events.push({id:s,callback:a,parameter:p}),a({eventName:"listenerRegistered",listenerId:s,data:!0,pingData:{gppVersion:"1.1",cmpStatus:"stub",cmpDisplayStatus:"hidden",signalStatus:"not ready",supportedAPIs:e,cmpId:10,sectionList:[],applicableSections:[-1],gppString:"",parsedSections:{}}},!0)}else if("removeEventListener"===n){for(var i=!1,o=0;o<__gpp.events.length;o++)if(__gpp.events[o].id==p){__gpp.events.splice(o,1),i=!0;break}a({eventName:"listenerRemoved",listenerId:p,data:i,pingData:{gppVersion:"1.1",cmpStatus:"stub",cmpDisplayStatus:"hidden",signalStatus:"not ready",supportedAPIs:e,cmpId:10,sectionList:[],applicableSections:[-1],gppString:"",parsedSections:{}}},!0)}else"hasSection"===n?a(!1,!0):"getSection"===n||"getField"===n?a(null,!0):__gpp.queue.push([].slice.apply(t))},window.__gpp_msghandler=function(e){var t="string"==typeof e.data;try{var n=t?JSON.parse(e.data):e.data}catch(e){n=null}if("object"==typeof n&&null!==n&&"__gppCall"in n){var a=n.__gppCall;window.__gpp(a.command,(function(n,p){var s={__gppReturn:{returnValue:n,success:p,callId:a.callId}};e.source.postMessage(t?JSON.stringify(s):s,"*")}),"parameter"in a?a.parameter:null,"version"in a?a.version:"1.1")}},"__gpp"in window&&"function"==typeof window.__gpp||(window.__gpp=window.__gpp_stub,window.addEventListener("message",window.__gpp_msghandler,!1),window.__gpp_addFrame("__gppLocator"))}();var s=function(){var e=arguments;typeof window.__uspapi!==s&&setTimeout((function(){void 0!==window.__uspapi&&window.__uspapi.apply(window.__uspapi,e)}),500)};if(void 0===window.__uspapi){window.__uspapi=s;var i=setInterval((function(){p++,window.__uspapi===s&&p<3?console.warn("USP is not accessible"):clearInterval(i)}),6e3)}}();
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||
</script>
|
||
|
||
<script async>
|
||
(function(w, d, s, l, i) {
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||
w[l] = w[l] || [];
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||
w[l].push({'gtm.start': new Date().getTime(), event: 'gtm.js'});
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||
var f = d.getElementsByTagName(s)[0], j = d.createElement(s), dl = l != 'dataLayer' ? '&l=' + l : '';
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||
j.async = true;
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||
j.src = 'https://www.googletagmanager.com/gtm.js?id=' + i + dl;
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||
f.parentNode.insertBefore(j,f);
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||
})(window, document, 'script', 'dataLayer', 'GTM-K25QL22');
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||
</script>
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||
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||
<script>
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||
window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
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||
ga('create', 'UA-410289-1', 'auto');
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||
ga('require', 'linkid', 'linkid.js');
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||
ga('require', 'GTM-TSD9TZP');
|
||
ga('set', 'siteSpeedSampleRate', 25);
|
||
</script>
|
||
<script async src="https://www.google-analytics.com/analytics.js"></script>
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||
<script async>var analytics = {
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||
'dimension1': 'Frameworks',
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||
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||
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||
'dimension4': '0',
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||
'dimension5': '',
|
||
'dimension7': 'Site reliability engineering, framework',
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||
'dimension8': 'gvarun',
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||
'dimension9': 'undefined',
|
||
'dimension10': 'Meta'
|
||
};
|
||
|
||
if (window.ga) {
|
||
Object.keys(analytics).forEach(function(key) {
|
||
window.ga('set', key, analytics[key]);
|
||
});
|
||
|
||
window.ga('send', 'pageview');
|
||
}</script>
|
||
|
||
<script src="https://dz2cdn1.dzone.com/themes/dz20/lib/static/jquery/jquery.min.js"></script>
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||
<script async src="https://dz2cdn1.dzone.com/themes/dz20/lib/static/bootstrap/bootstrap.min.js"></script>
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||
|
||
<script>
|
||
function loadScript(src) {
|
||
return new Promise(function (resolve, reject) {
|
||
const s = document.createElement('script');
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||
s.src = src;
|
||
s.onload = resolve;
|
||
s.onerror = reject;
|
||
document.head.appendChild(s);
|
||
});
|
||
}
|
||
|
||
function loadStyle(href) {
|
||
const link = document.createElement('link');
|
||
link.rel = 'stylesheet';
|
||
link.href = href;
|
||
document.head.appendChild(link);
|
||
}
|
||
|
||
function loadScriptsSync(deferred) {
|
||
var p = Promise.resolve();
|
||
for (var i = 0; i < deferred.length; i++) {
|
||
let script = deferred[i];
|
||
p = p.then(function() {
|
||
return loadScript(script);
|
||
});
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||
}
|
||
return p;
|
||
}
|
||
|
||
function loadStyles() {
|
||
const deferred = [
|
||
'https://dz2cdn1.dzone.com/themes/dz20/lib/codemirror/lib/codemirror.css',
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||
'https://dz2cdn1.dzone.com/themes/dz20/ftl/comments/styles.css',
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||
'https://dz2cdn1.dzone.com/themes/dz20/lib/froala3/css/froala_editor.pkgd.min.css',
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||
'https://dz2cdn1.dzone.com/themes/dz20/lib/froala3/css/themes/gray.min.css',
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'https://dz2cdn1.dzone.com/themes/dz20/ftl/article/mini-profile.css'
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||
];
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||
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||
for (var i = 0; i < deferred.length; i++) {
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||
loadStyle(deferred[i]);
|
||
}
|
||
}
|
||
|
||
window.addEventListener('load', function(_event) {
|
||
loadStyles();
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||
|
||
loadScriptsSync([
|
||
'https://dz2cdn1.dzone.com/themes/dz20/lib/lazysizes.min.js',
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||
'https://dz2cdn1.dzone.com/themes/dz20/ftl/article/codeblocks.js',
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||
'https://dz2cdn1.dzone.com/themes/dz20/ftl/article/activity-bar.js',
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||
'https://dz2cdn1.dzone.com/themes/dz20/lib/froala3/js/froala_editor.pkgd.min.js',
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||
'https://dz2cdn1.dzone.com/themes/dz20/ftl/froala/content.js',
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||
'https://dz2cdn1.dzone.com/themes/dz20/ftl/comments/content.js',
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||
'https://dz2cdn1.dzone.com/themes/dz20/ftl/article/content.js'
|
||
]);
|
||
});
|
||
</script>
|
||
</body>
|
||
</html>
|